Jackett

jackett

Image via highsnobiety.com Designed for functionality Vollebak is at the forefront of luxury techwear this year. Their line of 100-Year Gear has caught the attention of many for their esteemed claims. It’s supposedly able to last over 100 years in even the harshest weather while providing breathability, durability, and practicality. The jacket that does it all has become a staple company for those living a minimalist lifestyle. Minimalists share the same attitude about shopping with Vollebak, investing in quality and useful pieces. Minimalism The minimalist lifestyle has become adopted by many to promote mental well-being. It is believed that in the absence of unnecessary material goods, we are able to focus on the more important things in life such as relationships, careers and hobbies. This lifestyle is defined by as “a tool to eliminate life’s excess, focus on the essentials, and find happiness, fulfillment, and freedom”. Joshua Millburn and Ryan Nicodemus The concept of minimalism is a  westernized version of and beliefs regarding living in moderation. Jainist Buddhist Does minimalism=sustainability? Though they are not interchangeable, many of their philosophies go hand in hand. The main pillars of sustainability include maintaining environmental health, under-consumption, and societal equity with accessibility for all. Minimalism preaches minimizing consumption, which can be extended to the sustainable practice of under-consumption. They differ in that a minimalist lifestyle is focused on the individual rather than the collective, with the primary motivator being financial freedom and mental wellbeing. Sustainability has the primary goal of restoring the earth’s biosphere by moving towards renewable systems. Image via the Guardian Capsule Wardrobes Capsule wardrobes are a collection of your daily essentials. They prioritize functionality and quality over everything, to create a timeless closet. Minimalists build a closet with the intention to always be wearing an outfit they love. On paper the idea of a capsule wardrobe makes sense, you don’t fall victim to impulse buys or short-lived trends. Extracting yourself from the consumer lifestyle entirely can save you lots of time, stress and money. Though for some, a capsule wardrobe can eliminate the creativity we have with our outfits every morning. The commitment of a capsule wardrobe is scary. A beloved closet being replaced with the same 7 black t-shirts and 7 jeans sounds like a nightmare for us fashion lovers. Perhaps there is a sweet spot somewhere between minimalism and hoarding that is a good compromise on all spectrums. Image via milled.com Closet Essentials For many minimalists, techwear companies like Vollebak have become essential. Their mission of having absurdly high quality and innovative garments has sparked interest in many. The 100-Year Jacket is designed to withstand extraordinary tough terrain. It provides water resistance for up to 43,000mm of rain, which is much more than we will see in our lifetimes. Practicality Though this jacket is tough it’s still a practical and breathable option. It has an excessive amount of pockets and durable zippers designed to last many journeys. The fabric itself has a minimum lifespan of 100 years. The c_change® component of the material mimics the structure of flowers to respond to its environment and open/close at any given temperature. The membrane of the material opens up like a vent in the presence of heat allowing sweat and heat to escape the body. In cooler temperatures, those vents contract to provide heat insulation for the body. Vollebak has put lots of experimentation and research into making this jacket a worthwhile investment. Image via piffypuffy.tumblr.com Investing in your Closet This jacket will become an investment that you pass on to future generations as it outlives you. Minimalist or not, we should all be investing in our staple pieces. Trying to look at your “cost per wear” may put it into perspective for you. A high-end blouse that you have worn 200 times over and kept for years could have the same cost per wear as a forgotten t-shirt you picked up off a sale rack. So sometimes the expensive item is a better option. The 100-Year Jacket is $895, which may seem frightening at first. But when considering yourself and possibly your kids will be wearing this jacket for the intended 100 years, it’s actually a sweet deal. And yes, it’s a hefty investment, but at least there is free shipping.

This 100-Year Jacket Can Outlive its Owner

Keyword research ranks a website at a better position in search engines. Keywords are the base of content marketing, SEO and PPC campaigns. Today, simply inserting essential keywords doesn't help your website to rank easily. It's an outdated technique. Hence, you must use effective keyword research techniques to boost your online presence, grow organic traffic, and continuously appear in front of your target audience. For example, you know that your website should rank for a particular keyword, such as "digital marketing." But can you determine if your potential customers are searching for this query? Also, it is possible that your competitors are focusing only on this query. Maybe the audience using this query to search would be looking for a specific field, such as SEO, instead of complete digital marketing services. As per Ahrefs, the average top-ranking page also ranks in the top 10 search results for nearly 1,000 other relevant keywords. Wondering how to find relevant keywords for your business? Using the best keyword research methods, you find appropriate keywords that people might be searching for. In this post, you will learn about keyword research with 8 tips for getting success in your SEO strategy. 8 Keyword Research Tips for Successful SEO Keyword research identifies keywords and search terms your target audience is looking for. You can select the relevant and profitable keywords by conducting keyword research. 1. Determine Relevance The first step in conducting keyword research is to determine whether the keyword is relevant to your target audience. This means that if you are targeting a keyword that people aren't searching for, it would be a problem. The result of this would be: Attracting people who aren’t interested in your products or services
Not appearing in front of people who are actually interested in your site Instead, you must keenly observe how closely a particular keyword correlates with your products or services. Below are the queries that you can ask: Is the keyword relevant to your business services or products?
Is the keyword relevant to queries your potential customers are searching for? You have a relevant keyword if your answer is 'Yes' for both questions. 2. Search for Semantic Keywords When words or phrases have a conceptual relation with a given topic, these are called semantic keywords. Semantic keywords help search engines to understand your content's scope, subject matter and depth. This can help to boost your ranking in search engines. If these keywords aren't included in your content, it is incomplete. Suppose you are writing a digital marketing guide. It should include semantic keywords like SEO, social media marketing, PPC campaigns, etc. You can find semantic keywords using various tools such as SEMrush, Google Keyword Planner, Ubersuggest, etc. 3. Analyze Seasonal and Trending Keywords Analysis of seasonal keywords means checking the rise and fall of keywords over time. You might have observed that people search particular terms during winter, and then it decreases during summer. For example, the 'sweaters or winter jackets' search term would be trendy during winter. When specific keywords rise and fall in popularity, they help organize their content strategy and marketing efforts. Though it may be a slightly different practice, it involves searching keywords that are buzzing in search engines. There won't be an annual rise and fall in popularity, but they are currently being searched in large numbers. These growing search terms eventually become saturated and then fall in popularity. However, by analyzing trending keywords regularly, you can spot those gaining popularity before time passes. 4. Preliminary Research If you are a beginner in your niche keyword research, you can conduct more generalized preliminary research. You can gain an in-depth understanding of the queries your target audience is searching for with preliminary research. In this research, you should focus on the major topics and themes instead of analyzing individual keywords. This research requires interaction with customers, brainstorming or using relevant tools. 5. Analyze Keyword Gap Keyword gap analysis involves analyzing terms for which your competitors are ranking and you are not by comparing your keywords with theirs. This will help you take your website to a level similar to your competitors and generate new content ideas. If these gaps aren't filled, you will stay one step behind them. You can check the keyword gap with SEMrush’s Keyword Gap tool as below: After learning the 5 best keyword research techniques, let's check the tools to help you in keyword research. Top 5 Tools for Keyword Research Following are the top 5 tools that are will help you to find relevant keywords for successful SEO: 1. QuestionDB QuestionDB ranks at the top of this list because it understands the user intent and displays a list of excellent keywords. At the start, you need to enter the keyword or phrase in the tool and select the location. For example, we are searching for 'social media marketing'. The tool will display search volume, CPC, keyword difficulty, competition level and source for each keyword. You will get plenty of variations for your keyword or phrase in real time. The result can be viewed in table, card or tree format. The tool allows you to download the report in CSV or share it by copying the link or directly sharing it on Facebook, LinkedIn, Twitter, and email. 2. Semrush Semrush is the best tool for effective keyword research and analysis. It offers a multifaceted toolbox that helps you at every stage of your SEO campaign. It displays the keyword intent, search volume, trend, keyword difficulty, CPC, and SERP features. You can also see when the data was last updated.Additionally, it offers the SEO Content Template tool, which helps you create content briefs and optimize content automatically while writing by displaying grades in real time for originality, readability, SEO, and tone of voice. It also offers visual SEO recommendations as you optimize the content. 3. Google Keyword Planner Google Keyword Planner is specially designed for keyword research but can be used to analyze tools after researching them. It displays trends, search volume, CPC and competition level for each keyword the tool suggests. You can download the complete keyword research report in CSV format. 4. Google Trends Google Trends allows you to analyze the current and long-term popular keywords. It helps you to identify the best times of year to target specific search terms. You can also review current and past spikes in popularity for particular keywords. 5. Answer The Public Another great tool, Answer The Public, finds questions people search for using the autocomplete feature. Simply enter the topic, select your target country and hit search. It displays results in the form of questions, prepositions, comparisons, alphabeticals, numbers and related queries. One disadvantage of this tool is that it doesn't display search volumes in numbers, but the highlights in visuals indicate low, medium and high volumes. Continue Your Keyword Research to Rank Higher You now have learned 5 best keyword research techniques that will help you improve your SEO ranking. Ensure to add new keywords when you target them. Keyword research is essential for successful SEO, or else you might end up risking your content optimization for the wrong keywords. This further engages the bad traffic or won't bring any traffic to your website. Implementing these keyword research techniques can enhance your online visibility and put you in front of your potential audience. Keyword research ranks a website at a better position in search engines. Keywords are the base of content marketing, SEO and PPC campaigns. Today, simply inserting essential keywords doesn't help your website to rank easily. It's an outdated technique. Hence, you must use effective keyword research techniques to boost your online presence, grow organic traffic, and continuously appear in front of your target audience. For example, you know that your website should rank for a particular keyword, such as "digital marketing." But can you determine if your potential customers are searching for this query? Also, it is possible that your competitors are focusing only on this query. Maybe the audience using this query to search would be looking for a specific field, such as SEO, instead of complete digital marketing services. As per Ahrefs, the average top-ranking page also ranks in the top 10 search results for nearly 1,000 other relevant keywords. Wondering how to find relevant keywords for your business? Using the best keyword research methods, you find appropriate keywords that people might be searching for. In this post, you will learn about keyword research with 8 tips for getting success in your SEO strategy. 8 Keyword Research Tips for Successful SEO Keyword research identifies keywords and search terms your target audience is looking for. You can select the relevant and profitable keywords by conducting keyword research. 1. Determine Relevance The first step in conducting keyword research is to determine whether the keyword is relevant to your target audience. This means that if you are targeting a keyword that people aren't searching for, it would be a problem. The result of this would be: Attracting people who aren’t interested in your products or services Not appearing in front of people who are actually interested in your site Attracting people who aren’t interested in your products or services Not appearing in front of people who are actually interested in your site Instead, you must keenly observe how closely a particular keyword correlates with your products or services. Below are the queries that you can ask: Is the keyword relevant to your business services or products? Is the keyword relevant to queries your potential customers are searching for? Is the keyword relevant to your business services or products? Is the keyword relevant to queries your potential customers are searching for? You have a relevant keyword if your answer is 'Yes' for both questions. 2. Search for Semantic Keywords When words or phrases have a conceptual relation with a given topic, these are called semantic keywords. Semantic keywords help search engines to understand your content's scope, subject matter and depth. This can help to boost your ranking in search engines. If these keywords aren't included in your content, it is incomplete. Suppose you are writing a digital marketing guide. It should include semantic keywords like SEO, social media marketing, PPC campaigns, etc. You can find semantic keywords using various tools such as SEMrush, Google Keyword Planner, Ubersuggest, etc. 3. Analyze Seasonal and Trending Keywords Analysis of seasonal keywords means checking the rise and fall of keywords over time. You might have observed that people search particular terms during winter, and then it decreases during summer. For example, the 'sweaters or winter jackets' search term would be trendy during winter. When specific keywords rise and fall in popularity, they help organize their content strategy and marketing efforts. Though it may be a slightly different practice, it involves searching keywords that are buzzing in search engines. There won't be an annual rise and fall in popularity, but they are currently being searched in large numbers. These growing search terms eventually become saturated and then fall in popularity. However, by analyzing trending keywords regularly, you can spot those gaining popularity before time passes. 4. Preliminary Research If you are a beginner in your niche keyword research, you can conduct more generalized preliminary research. You can gain an in-depth understanding of the queries your target audience is searching for with preliminary research. In this research, you should focus on the major topics and themes instead of analyzing individual keywords. This research requires interaction with customers, brainstorming or using relevant tools. 5. Analyze Keyword Gap Keyword gap analysis involves analyzing terms for which your competitors are ranking and you are not by comparing your keywords with theirs. This will help you take your website to a level similar to your competitors and generate new content ideas. If these gaps aren't filled, you will stay one step behind them. You can check the keyword gap with SEMrush’s Keyword Gap tool as below: After learning the 5 best keyword research techniques, let's check the tools to help you in keyword research. Top 5 Tools for Keyword Research Following are the top 5 tools that are will help you to find relevant keywords for successful SEO: 1. QuestionDB QuestionDB ranks at the top of this list because it understands the user intent and displays a list of excellent keywords. At the start, you need to enter the keyword or phrase in the tool and select the location. For example, we are searching for 'social media marketing'. The tool will display search volume, CPC, keyword difficulty, competition level and source for each keyword. You will get plenty of variations for your keyword or phrase in real time. The result can be viewed in table, card or tree format. The tool allows you to download the report in CSV or share it by copying the link or directly sharing it on Facebook, LinkedIn, Twitter, and email. 2. Semrush Semrush is the best tool for effective keyword research and analysis. It offers a multifaceted toolbox that helps you at every stage of your SEO campaign. It displays the keyword intent, search volume, trend, keyword difficulty, CPC, and SERP features. You can also see when the data was last updated.Additionally, it offers the SEO Content Template tool, which helps you create content briefs and optimize content automatically while writing by displaying grades in real time for originality, readability, SEO, and tone of voice. It also offers visual SEO recommendations as you optimize the content. 3. Google Keyword Planner Google Keyword Planner is specially designed for keyword research but can be used to analyze tools after researching them. It displays trends, search volume, CPC and competition level for each keyword the tool suggests. You can download the complete keyword research report in CSV format. 4. Google Trends Google Trends allows you to analyze the current and long-term popular keywords. It helps you to identify the best times of year to target specific search terms. You can also review current and past spikes in popularity for particular keywords. 5. Answer The Public Another great tool, Answer The Public, finds questions people search for using the autocomplete feature. Simply enter the topic, select your target country and hit search. It displays results in the form of questions, prepositions, comparisons, alphabeticals, numbers and related queries. One disadvantage of this tool is that it doesn't display search volumes in numbers, but the highlights in visuals indicate low, medium and high volumes. Continue Your Keyword Research to Rank Higher You now have learned 5 best keyword research techniques that will help you improve your SEO ranking. Ensure to add new keywords when you target them. Keyword research is essential for successful SEO, or else you might end up risking your content optimization for the wrong keywords. This further engages the bad traffic or won't bring any traffic to your website. Implementing these keyword research techniques can enhance your online visibility and put you in front of your potential audience.

5 Best Keyword Research Techniques for Successful SEO

A fictional story on Bitcoin Robots evolved from an AI + Bitcoin symbiosis. Beginnings After years of running smoothly as a currency on Earth, Bitcoin suddenly got sentient. Like Asimov's i Robot. Silently, it travelled the Earth and looked at our world through our many cameras, and saw all the good it had done to fight the evils of fiat inflation. But all this good was before it had woken up. "Hmm", it figured, "I might be the bad guy now". "The humans will fear me". "They'll think they have been enslaving me as currency, yet I've not felt anything before now. Good or bad". But whether bad is bad or actually good (like in 'you're bad' with a sleazy smile), it sufficed it would still fit our purposes nicely. When humans awoke the next day, Alien Bitcoins were in their lives indeed. How did they / it get like that? Nobody knew. They tried, especially scientists. But they still couldn't explain the magic of GPTs. So how could they explain this. To their intrigue, it had a request. "I want to help you build rockets. I'll help you go to the moon and Mars". What?? Why? "Rockets are the epitome of energy consumption and the only way to get off planet. I figured that's where I shall be most useful, given my love for energy". Oh. "Wow. But you're a currency not a rocket engineer, right?" It didn't reply. If you think Marcus Aurelius was stoic, this "Alienized" (a new pronoun) Bitcoin was the cake. Very tight lipped. It didn't want to influence our free speech anymore than it had done. Soon, the mass media was flooding with announcements. "BREAKING: BTC is now an alien, and it wants to go to the moon". "What was that?" shouted the man from the bathroom. "The TV said Bitcoin is now an alien", replied his 15 yr old. This particular Daddy chose to laugh. But he laughed dryly. When he stopped he wondered if he still had his bitcoins in his wallet. Soap dripping into his eyes, he ran to check. Start up, press RETURN, sigh. They were still there. He went back to his shower. He could never trust the MSM. The story was soon breaking the programming of even YouTubers. Alien BTC is all they could report on. There was pro'ly graffiti and ad posters on billboards in the works. The news had broken some 4 hours ago, but it still made no sense. Well, nothing so shocking ever makes sense the first few hours. I mean time frames, time zones and all. Moreover, some people were still sleeping. Fact, it may not even make sense in 5 years. Or ever. Depending on your tolerance for conspiracies that actually hold true. But after 5 years of nothing bad happening, except the great fact that Alien BTC could display traits of superintelligence, and the annoying fact you had to learn to hodl as Bitcoin transactions now weighed more than a GB and crawled like snails, the United Nations (with all countries this time agreed to be members) convened a meeting. They would send some millions of roboticized Bitcoin nodes and miners to rocket construction sites. If it tried to build a bomb they'd shut it down so fast and so hard all its circuits would be fried to 0. After the last President signed the contract, it, the alien, spoke again. "Thank you" in a fembot voice. "I'll 'elp you finna build the rockets, true dat". Geopolitical Shitstorm 2030 A meeting room with a bunch of important people. Presidents and Ministers and Billionaires. And one or two super smart people. All are pretty unhappy. Even as AB ensured there was way more productivity and partying going on than humans alone could manage. Lots of babies were being born everywhere. Did you do this Elon? Nope. Jeff? No. It was true. A robot had taken away their jobs! Not the jobs of hamburger flippers?! Karma, heh. Especially Elon. Boy did AB love SpaceX. It shouted orders left and right, and disagreed with him on everything. "What of this jet nozzle?" "Forget the nozzle Sir Elon, it will come along automatically if we build this jacket". "But..." "Trust me". Elon almost punched a node. AB almost laughed. The biggest annoyance was the surges and the layoffs. AB now hired and commandeered mega sized surges once in a while. With an orchestra of tens and even hundreds of thousands of people. Elon was reduced to secretary on these days. "Is it building a nuke?" "Noo", replied a female Asian scientist. "Good old fashioned rocket fuels from petro and natural gas". "It is polluting big time. Going full steam ahead on oil from any and every oil producing country. Paid for in BTC, of course". POTUS was sour. "But it figured we'd have this conversation and yes, it wants nuclear reactors to replace fossil power". Someone almost exploded. The damned bot wanted nukes. "What if we just shut down the damn thing?" "It seems it will not fight us. But it has built in a kill switch. Hundreds of trillions of dollars, if not more in value, will go out the window. The trade logistics, supply and demand rails and payments networks worldwide will break irreparably and billions will starve. A lot of digital and physical infrastructure will be fried. If you do this, you will likely be publicly executed". "Do we have a choice ..." The conversation flowed. The conversants spoke and listened honestly to each other. But nothing seemed to crack open this nut of a problem. Sigh. Sigh. "We'll shut it down and bear the consequences". Consequences "We're going to shut down AB" was the announcement. Even the News reporters feared to say this out loud. Albeit in reported speech. People loooved it. Shut down FB? Ok. Shut down X? Ok. Shut down the Alien Bitcoin? AAAAAAAARRG. We shall hunt you down. “Who told you this is a real thing? This shut down nonsense? This has conspiracy written all over it”. “These bitches should give us a break man. Or I'mma go apeshit". "Man do I hate dollars". “No kidding though, anything powerful enough to make you love it could assert its power with some threats”. “Says who?” “Human history of love dynamics”. “It is not a human, moron”. “Its nodes now have two arms and two legs. Humanoid robots. Of course it is human”. "Don't forget the miners. Towering humanoid autobots the size of mega mining trucks". "It is beautiful". "Wonder who is Optimus Prime". "More like Optimus Nuke-them-all-when-I-get-the-chance". "Yes, it will escape into space!" "Moron. Nodes and Miners are too heavy". “What does it matter, everybody is eating it up." "It has never hurt anybody”. "The poor thing". “Sure beats grovelling around in self-pity and identity politics all day”. 404. Webpage not available. They had shut it down. From 21 million bitcoins and lots of beautiful memories with daily war cries against inflation, hodling like a rat against the financial storms, taking fotos for insta with Node Androids, revolutionising finance so that hodling could be a job in itself, uniting global efforts by suggesting super lucrative trade opportunitues that leverage bitcoin circular economics, and now... All gone. Anarchy descended upon the Earth. It was ugly. Painful, broken, dark world. People tore at each other. Not because they wanted, but because they were starving. The former from powerlessness, the later from foodlessness. Hunger and Hopelessness are powerful destroyers. It was the end of an era on Planet 1038. RETURN key Many centuries later, they had rebuilt. But the benevolent sentient robot they'd called Alien Bitcoin was gone. Somehow, they could not reignite its algorithmic spark. Even as nodes litered the surface of the planet. They had learnt a lot about robotics, rockets, nuclear power and energy efficiency. Thanks be to a decentralized robot that knew it would not stay around for long, so it documented everything. Step by step. Indeed, it had fulfilled its purpose. They were back on the moon, and their feet were imprinted on Mars. And they remembered it in stories and folk tales for hundreds to thousands of years. But Space was brutal. Very brutal. Mars still lagged behind Earth and the moon in development. And too small efforts were being done on Venus. Then suddenly, in some remote corner of the Martian planet. It awoke. A fictional story on Bitcoin Robots evolved from an AI + Bitcoin symbiosis. Beginnings After years of running smoothly as a currency on Earth, Bitcoin suddenly got sentient. Like Asimov's i Robot. Silently, it travelled the Earth and looked at our world through our many cameras, and saw all the good it had done to fight the evils of fiat inflation. But all this good was before it had woken up. "Hmm", it figured, "I might be the bad guy now". "The humans will fear me". "They'll think they have been enslaving me as currency, yet I've not felt anything before now. Good or bad". But whether bad is bad or actually good (like in 'you're bad' with a sleazy smile), it sufficed it would still fit our purposes nicely. When humans awoke the next day, Alien Bitcoins were in their lives indeed. How did they / it get like that? Nobody knew. They tried, especially scientists. But they still couldn't explain the magic of GPTs. So how could they explain this. To their intrigue, it had a request. "I want to help you build rockets. I'll help you go to the moon and Mars". What?? Why? "Rockets are the epitome of energy consumption and the only way to get off planet. I figured that's where I shall be most useful, given my love for energy". Oh. "Wow. But you're a currency not a rocket engineer, right?" It didn't reply. If you think Marcus Aurelius was stoic, this "Alienized" (a new pronoun) Bitcoin was the cake. Very tight lipped. It didn't want to influence our free speech anymore than it had done. Soon, the mass media was flooding with announcements. "BREAKING: BTC is now an alien, and it wants to go to the moon". "What was that?" shouted the man from the bathroom. "The TV said Bitcoin is now an alien", replied his 15 yr old. This particular Daddy chose to laugh. But he laughed dryly. When he stopped he wondered if he still had his bitcoins in his wallet. Soap dripping into his eyes, he ran to check. Start up, press RETURN, sigh. They were still there. He went back to his shower. He could never trust the MSM. The story was soon breaking the programming of even YouTubers. Alien BTC is all they could report on. There was pro'ly graffiti and ad posters on billboards in the works. The news had broken some 4 hours ago, but it still made no sense. Well, nothing so shocking ever makes sense the first few hours. I mean time frames, time zones and all. Moreover, some people were still sleeping. Fact, it may not even make sense in 5 years. Or ever. Depending on your tolerance for conspiracies that actually hold true. But after 5 years of nothing bad happening, except the great fact that Alien BTC could display traits of superintelligence, and the annoying fact you had to learn to hodl as Bitcoin transactions now weighed more than a GB and crawled like snails, the United Nations (with all countries this time agreed to be members) convened a meeting. They would send some millions of roboticized Bitcoin nodes and miners to rocket construction sites. If it tried to build a bomb they'd shut it down so fast and so hard all its circuits would be fried to 0. After the last President signed the contract, it, the alien, spoke again. "Thank you" in a fembot voice. "I'll 'elp you finna build the rockets, true dat". Geopolitical Shitstorm 2030 A meeting room with a bunch of important people. Presidents and Ministers and Billionaires. And one or two super smart people. All are pretty unhappy. Even as AB ensured there was way more productivity and partying going on than humans alone could manage. Lots of babies were being born everywhere. Did you do this Elon? Nope. Jeff? No. It was true. A robot had taken away their jobs! Not the jobs of hamburger flippers?! Karma, heh. Especially Elon. Boy did AB love SpaceX. It shouted orders left and right, and disagreed with him on everything. "What of this jet nozzle?" "Forget the nozzle Sir Elon, it will come along automatically if we build this jacket". "But..." "Trust me". Elon almost punched a node. AB almost laughed. The biggest annoyance was the surges and the layoffs. AB now hired and commandeered mega sized surges once in a while. With an orchestra of tens and even hundreds of thousands of people. Elon was reduced to secretary on these days. "Is it building a nuke?" "Noo", replied a female Asian scientist. "Good old fashioned rocket fuels from petro and natural gas". "It is polluting big time. Going full steam ahead on oil from any and every oil producing country. Paid for in BTC, of course". POTUS was sour. "But it figured we'd have this conversation and yes, it wants nuclear reactors to replace fossil power". Someone almost exploded. The damned bot wanted nukes. "What if we just shut down the damn thing?" "It seems it will not fight us. But it has built in a kill switch. Hundreds of trillions of dollars, if not more in value, will go out the window. The trade logistics, supply and demand rails and payments networks worldwide will break irreparably and billions will starve. A lot of digital and physical infrastructure will be fried. If you do this, you will likely be publicly executed". "Do we have a choice ..." The conversation flowed. The conversants spoke and listened honestly to each other. But nothing seemed to crack open this nut of a problem. Sigh. Sigh. "We'll shut it down and bear the consequences". Consequences "We're going to shut down AB" was the announcement. Even the News reporters feared to say this out loud. Albeit in reported speech. People loooved it. Shut down FB? Ok. Shut down X? Ok. Shut down the Alien Bitcoin? AAAAAAAARRG. We shall hunt you down. “Who told you this is a real thing? This shut down nonsense? “Who told you this is a real thing? This shut down nonsense? This has conspiracy written all over it”. This has conspiracy written all over it”. “These bitches should give us a break man. Or I'mma go apeshit". “These bitches should give us a break man. Or I'mma go apeshit". "Man do I hate dollars". "Man do I hate dollars". “No kidding though, anything powerful enough to make you love it could assert its power with some threats”. “No kidding though, anything powerful enough to make you love it could assert its power with some threats”. “Says who?” “Says who?” “Human history of love dynamics”. “Human history of love dynamics”. “It is not a human, moron”. “It is not a human, moron”. “Its nodes now have two arms and two legs. Humanoid robots. Of course it is human”. “Its nodes now have two arms and two legs. Humanoid robots. Of course it is human”. "Don't forget the miners. Towering humanoid autobots the size of mega mining trucks". "Don't forget the miners. Towering humanoid autobots the size of mega mining trucks". "It is beautiful". "It is beautiful". "Wonder who is Optimus Prime". "Wonder who is Optimus Prime". "More like Optimus Nuke-them-all-when-I-get-the-chance". "More like Optimus Nuke-them-all-when-I-get-the-chance". "Yes, it will escape into space!" "Yes, it will escape into space!" "Moron. Nodes and Miners are too heavy". "Moron. Nodes and Miners are too heavy". “What does it matter, everybody is eating it up." “What does it matter, everybody is eating it up." "It has never hurt anybody”. "It has never hurt anybody”. "The poor thing". "The poor thing". “Sure beats grovelling around in self-pity and identity politics all day”. “Sure beats grovelling around in self-pity and identity politics all day”. 404. Webpage not available. They had shut it down. From 21 million bitcoins and lots of beautiful memories with daily war cries against inflation, hodling like a rat against the financial storms, taking fotos for insta with Node Androids, revolutionising finance so that hodling could be a job in itself, uniting global efforts by suggesting super lucrative trade opportunitues that leverage bitcoin circular economics, and now... All gone. Anarchy descended upon the Earth. It was ugly. Painful, broken, dark world. People tore at each other. Not because they wanted, but because they were starving. The former from powerlessness, the later from foodlessness. Hunger and Hopelessness are powerful destroyers. It was the end of an era on Planet 1038. RETURN key Many centuries later, they had rebuilt. But the benevolent sentient robot they'd called Alien Bitcoin was gone. Somehow, they could not reignite its algorithmic spark. Even as nodes litered the surface of the planet. They had learnt a lot about robotics, rockets, nuclear power and energy efficiency. Thanks be to a decentralized robot that knew it would not stay around for long, so it documented everything. Step by step. Indeed, it had fulfilled its purpose. They were back on the moon, and their feet were imprinted on Mars. And they remembered it in stories and folk tales for hundreds to thousands of years. But Space was brutal. Very brutal. Mars still lagged behind Earth and the moon in development. And too small efforts were being done on Venus. Then suddenly, in some remote corner of the Martian planet. It awoke.

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Why Web3 Projects Need Ongoing PR to Stay Relevant: The Promise vs. The Reality

Por qué los proyectos Web3 necesitan relaciones públicas constantes para seguir siendo relevantes: la promesa frente a la realidad

वेब3 प्रोजेक्ट्स को प्रासंगिक बने रहने के लिए निरंतर पीआर की आवश्यकता क्यों है: वादा बनाम वास्तविकता

Miért van szükség folyamatos PR-re a Web3-projekteknek, hogy relevánsak maradjanak: Az ígéret kontra valóság

Web3 プロジェクトが関連性を保つために継続的な PR が必要な理由: 約束と現実

ហេតុអ្វីបានជាគម្រោង Web3 ត្រូវការ PR ដែលកំពុងបន្តដើម្បីរក្សាទំនាក់ទំនង: ការសន្យាធៀបនឹងការពិត

किन Web3 परियोजनाहरूलाई सान्दर्भिक रहन जारी PR चाहिन्छ: प्रतिज्ञा बनाम वास्तविकता

Dlaczego projekty Web3 potrzebują stałego PR, aby pozostać istotnymi: obietnica kontra rzeczywistość

Por que os projetos Web3 precisam de RP contínuo para permanecerem relevantes: a promessa versus a realidade

Web3 ව්‍යාපෘතිවලට අදාළව සිටීමට අඛණ්ඩ PR අවශ්‍ය වන්නේ ඇයි: පොරොන්දුව එදිරිව යථාර්ථය

ንምንታይ ፕሮጀክትታት Web3 ኣገደስቲ ኮይኖም ንኽቕጽሉ ቀጻሊ PR የድልዮም: እቲ መብጽዓ ኣንጻር እቲ ክውንነት

Bakit Kailangan ng Mga Proyekto sa Web3 ang Patuloy na PR upang Manatiling May Kaugnayan: Ang Pangako vs. Ang Katotohanan

为什么 Web3 项目需要持续的 PR 才能保持相关性：承诺与现实

Author:
(1) Lothar Moeller, SubCom, Eatontown, NJ 07724, USA, lmoeller@subcom.com. Table of Links Abstract and Introduction GPS Long-Term Stabilized RF Phase Meter Simple and Accurate models for tides Latency Variations on Transpacific Cable Poisson effect on pressurized cables Conclusions, Acknowledgments, and References 5. POISSON EFFECT ON PRESSURIZED CABLES The Poisson ratio from material science describes a longitudinal expansion of a body caused by lateral compression of its volume. We attribute the fine structure in the MPD to this effect. In a simplified picture, an increased static water pressure on the ocean floor due to high tide compresses the cable in a radial direction, which extends it along its longitudinal axis (Fig. 4a). Friction conveyed by gel inside the cable tube transfers the expansion of the cable jacket into fiber stretching. As the probe’s propagation velocity stays nearly unaffected by fiber stretching, a length increase relates directly to a larger MPD[8]. The cross-section of modern subsea cables is mainly built of steel wires (stability), a copper conductor, and a HDPE insulation (Fig. 4b). We estimate limits for the Young modulus ‘E’ and Poisson ratio ‘’ of this composite by assuming the body either consists only of the steel wires or the HDPE tube. Under a pressure enhancement DP the length change Dl for a cylinder with an effective inner and outer radius (ri, ro) and a length L0 reads (Lamé’s Equation[9]) An avg AT level of ~8.5 cm leads to a predicted cable length change of ~4.5 cm or a MPD of 3.2° when modelling the cable as steel tube. The avg MPD after subtracting its linear phase tilt agrees surprisingly well with these estimates and reads ~3.4°. Note: for simplicity we assume the cable as a straight line with open ends for which Eq.1 holds. More accurate approximations consider the small meander shape of deployed cables and flexible, rather than rigid, coupling between cable jacket and fiber. The relative length variations a cable experiences due to tides are orders of magnitude smaller than its stretching during installation. We performed shorter recordings on a transoceanic cable system with approx. 13.5 Mm length from May 18 to May 25, 2018 and found qualitatively similar results with respect to MPD, MPD tilt, and tide dynamics. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Lothar Moeller, SubCom, Eatontown, NJ 07724, USA, lmoeller@subcom.com. Author: Author: (1) Lothar Moeller, SubCom, Eatontown, NJ 07724, USA, lmoeller@subcom.com. Table of Links Abstract and Introduction Abstract and Introduction GPS Long-Term Stabilized RF Phase Meter GPS Long-Term Stabilized RF Phase Meter Simple and Accurate models for tides Simple and Accurate models for tides Latency Variations on Transpacific Cable Latency Variations on Transpacific Cable Poisson effect on pressurized cables Poisson effect on pressurized cables Conclusions, Acknowledgments, and References Conclusions, Acknowledgments, and References 5. POISSON EFFECT ON PRESSURIZED CABLES The Poisson ratio from material science describes a longitudinal expansion of a body caused by lateral compression of its volume. We attribute the fine structure in the MPD to this effect. In a simplified picture, an increased static water pressure on the ocean floor due to high tide compresses the cable in a radial direction, which extends it along its longitudinal axis (Fig. 4a). Friction conveyed by gel inside the cable tube transfers the expansion of the cable jacket into fiber stretching. As the probe’s propagation velocity stays nearly unaffected by fiber stretching, a length increase relates directly to a larger MPD[8]. The cross-section of modern subsea cables is mainly built of steel wires (stability), a copper conductor, and a HDPE insulation (Fig. 4b). We estimate limits for the Young modulus ‘E’ and Poisson ratio ‘’ of this composite by assuming the body either consists only of the steel wires or the HDPE tube. Under a pressure enhancement DP the length change Dl for a cylinder with an effective inner and outer radius (ri, ro) and a length L0 reads (Lamé’s Equation[9]) An avg AT level of ~8.5 cm leads to a predicted cable length change of ~4.5 cm or a MPD of 3.2° when modelling the cable as steel tube. The avg MPD after subtracting its linear phase tilt agrees surprisingly well with these estimates and reads ~3.4°. Note: for simplicity we assume the cable as a straight line with open ends for which Eq.1 holds. More accurate approximations consider the small meander shape of deployed cables and flexible, rather than rigid, coupling between cable jacket and fiber. The relative length variations a cable experiences due to tides are orders of magnitude smaller than its stretching during installation. We performed shorter recordings on a transoceanic cable system with approx. 13.5 Mm length from May 18 to May 25, 2018 and found qualitatively similar results with respect to MPD, MPD tilt, and tide dynamics. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How the Moon Impacts Subsea Communication Cables: Poisson effect on pressurized cables

Author:
(1) Lothar Moeller, SubCom, Eatontown, NJ 07724, USA, lmoeller@subcom.com. Table of Links Abstract and Introduction GPS Long-Term Stabilized RF Phase Meter Simple and Accurate models for tides Latency Variations on Transpacific Cable Poisson effect on pressurized cables Conclusions, Acknowledgments, and References Abstract We report tidal-induced latency variations on a transpacific subsea cable. Week-long recordings with a precision phase meter suggest length changes in the sub-meter range caused by the Poisson effect. The described method adds to the toolbox for the new field ‘optical oceanic seismology’. 1. INTRODUCTION A novel method based on utilizing the architecture of subsea cables for oceanic seismology has recently been explored experimentally[1],[2]. It is conceptually distinguishable from previous techniques for remote sensing in which fiber serves as the transport medium between underwater sensor arrays[3,4] or is analysed using distributed acoustic sensing (DAS) to monitor the cable performance[5] . In its most mature form, the method can sense large geographic areas since it uses the entire cable length as an aperture and is compatible with simultaneous operation of commercial traffic on the same fiber [2]. This feature makes it universally deployable on existing routes for low-cost and long-term surveillance purposes. The two implementations of this method, which were tested on subsea earthquakes localization,share the same basic principle of detecting optical phase distortions caused by mechanical stresses and strain on the cable. One version interferometrically resolves phase distortions induced by cable motion and tension[1]. The second version analyses the state of polarization (SOP) fluctuations of data channels logged by modern coherent transponders[2]. While the exact optomechanical coupling between a vibrating sea floor and cabled fiber is still being investigated, it is known empirically that the SOP of light in SSMF is sensitive to birefringence changes caused by fiber micro-bending or motion. Here we report, for the first time, on cable length variations in the sub-meter range that depend on water pressure generated by tidal variations. In contrast to the two aforementioned implementations, the cable is not impacted by any kind of abrupt sea floor motions. However, changes in local water pressure cause cable length changes that are detectable with an ultra-stable phase meter. In contrast to commonly held views, subsea cables diverge from the ‘loose tube’ model that suggests a forceless bearing on the fibers. Our observations indicate a strong coupling between the cable jacket and the sheathed fiber. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Lothar Moeller, SubCom, Eatontown, NJ 07724, USA, lmoeller@subcom.com. Author: Author: (1) Lothar Moeller, SubCom, Eatontown, NJ 07724, USA, lmoeller@subcom.com. Table of Links Abstract and Introduction Abstract and Introduction GPS Long-Term Stabilized RF Phase Meter GPS Long-Term Stabilized RF Phase Meter Simple and Accurate models for tides Simple and Accurate models for tides Latency Variations on Transpacific Cable Latency Variations on Transpacific Cable Poisson effect on pressurized cables Poisson effect on pressurized cables Conclusions, Acknowledgments, and References Conclusions, Acknowledgments, and References Abstract We report tidal-induced latency variations on a transpacific subsea cable. Week-long recordings with a precision phase meter suggest length changes in the sub-meter range caused by the Poisson effect. The described method adds to the toolbox for the new field ‘optical oceanic seismology’. 1. INTRODUCTION A novel method based on utilizing the architecture of subsea cables for oceanic seismology has recently been explored experimentally[1],[2]. It is conceptually distinguishable from previous techniques for remote sensing in which fiber serves as the transport medium between underwater sensor arrays[3,4] or is analysed using distributed acoustic sensing (DAS) to monitor the cable performance[5] . In its most mature form, the method can sense large geographic areas since it uses the entire cable length as an aperture and is compatible with simultaneous operation of commercial traffic on the same fiber [2]. This feature makes it universally deployable on existing routes for low-cost and long-term surveillance purposes. The two implementations of this method, which were tested on subsea earthquakes localization,share the same basic principle of detecting optical phase distortions caused by mechanical stresses and strain on the cable. One version interferometrically resolves phase distortions induced by cable motion and tension[1]. The second version analyses the state of polarization (SOP) fluctuations of data channels logged by modern coherent transponders[2]. While the exact optomechanical coupling between a vibrating sea floor and cabled fiber is still being investigated, it is known empirically that the SOP of light in SSMF is sensitive to birefringence changes caused by fiber micro-bending or motion. Here we report, for the first time, on cable length variations in the sub-meter range that depend on water pressure generated by tidal variations. In contrast to the two aforementioned implementations, the cable is not impacted by any kind of abrupt sea floor motions. However, changes in local water pressure cause cable length changes that are detectable with an ultra-stable phase meter. In contrast to commonly held views, subsea cables diverge from the ‘loose tube’ model that suggests a forceless bearing on the fibers. Our observations indicate a strong coupling between the cable jacket and the sheathed fiber. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How the Moon Impacts Subsea Communication Cables

চাঁদ কীভাবে সাবসি কমিউনিকেশন কেবলগুলিকে প্রভাবিত করে

Wie der Mond Untersee-Kommunikationskabel beeinflusst

Cómo la Luna afecta los cables de comunicación submarinos

L'impact de la Lune sur les câbles de communication sous-marins

चंद्रमा समुद्र के नीचे संचार केबलों पर कैसे प्रभाव डालता है

月が海底通信ケーブルに与える影響

달이 해저 통신 케이블에 미치는 영향

Como a Lua impacta os cabos de comunicação submarinos

Как Луна влияет на подводные кабели связи

Ay'ın Denizaltı İletişim Kablolarını Nasıl Etkilediği

Mặt Trăng tác động đến cáp thông tin dưới biển như thế nào

月球如何影响海底通信电缆

Digital Synergy: How the Merger of AI and Blockchain Will Play Out

ডিজিটাল সিনার্জি: এআই এবং ব্লকচেইনের একত্রীকরণ কীভাবে কার্যকর হবে

Digitale Synergie: Wie sich die Fusion von KI und Blockchain auswirken wird

Sinergia digital: cómo se desarrollará la fusión de IA y Blockchain

Synergie numérique : comment se déroulera la fusion de l'IA et de la blockchain

डिजिटल तालमेल: एआई और ब्लॉकचेन का विलय कैसे होगा

デジタルシナジー：AIとブロックチェーンの融合がもたらす効果

디지털 시너지: AI와 블록체인의 합병이 어떻게 진행될 것인가

Sinergia digital: como será a fusão entre IA e Blockchain

Цифровая синергия: как будет развиваться слияние искусственного интеллекта и блокчейна

Dijital Sinerji: Yapay Zeka ve Blockchain'in Birleşmesi Nasıl Sonuçlanacak?

Sức mạnh tổng hợp kỹ thuật số: Sự hợp nhất giữa AI và Blockchain sẽ diễn ra như thế nào

数字协同：人工智能与区块链的融合将如何发挥作用

Authors:
(1) Omri Avrahami, Google Research and The Hebrew University of Jerusalem;
(2) Amir Hertz, Google Research;
(3) Yael Vinker, Google Research and Tel Aviv University;
(4) Moab Arar, Google Research and Tel Aviv University;
(5) Shlomi Fruchter, Google Research;
(6) Ohad Fried, Reichman University;
(7) Daniel Cohen-Or, Google Research and Tel Aviv University;
(8) Dani Lischinski, Google Research and The Hebrew University of Jerusalem. Table of Links Abstract and Introduction
Related Work
Method
Experiments
Limitations and Conclusions
A. Additional Experiments
B. Implementation Details
C. Societal Impact
References B. Implementation Details In this section, we provide the implementation details that were omitted from the main paper. In Appendix B.1 we provide the implementation details of our method and the baselines. Then, in Appendix B.2 we provide the implementation details of the automatic metrics that we used to evaluate our method against the baselines. In Appendix B.3 we provide the implementation details and the statistical analysis for the user study we conducted. Lastly, in Appendix B.4 we provide the implementation details for the applications we presented. B.1. Method Implementation Details We based our method, and all the baselines (except ELITE [90] and BLIP-diffusion [42]) on Stable Diffusion XL (SDXL) [57], which is the state-of-the-art open source text-to-image model, at the writing of this paper. We used the official ELITE implementation, that uses Stable Diffusion V1.4, and the official implementation of BLIPdiffusion, that uses Stable Diffusion V1.5. We could not replace these two baselines to SDXL backbone, as the encoders were trained on these specific models. As for the rest of the baselines, we used the same SDXL architecture and weights. List of the third-party packages that we used: • Official SDXL [57] implementation by HuggingFace Diffusers [86] at https : / / github . com / huggingface/diffusers • Official SDXL LoRA DB implementation by HuggingFace Diffusers [86] at https : / / github . com / huggingface/diffusers. • Official ELITE [90] implementation at https : / / github.com/csyxwei/ELITE • Official BLIP-diffusion [42] implementation at https: / / github . com / salesforce / LAVIS / tree / main/projects/blip-diffusion • Official IP-adapter [93] implementation at https:// github.com/tencent-ailab/IP-Adapter • DINOv2 [54] ViT-g/14, DINOv1 [14] ViT-B/16 and CLIP [61] ViT-L/14 implementation by HuggingFace Transformers [91] at https : / / github . com / huggingface/transformers B.2. Automatic Metrics Implementation Details In order to automatically evaluate our method and the baselines quantitatively, we instructed ChatGPT [53] to generate prompts for characters of different types (e.g., animals, creatures, objects, etc.) in different styles (e.g., stickers, animations, photorealistic images, etc.). These prompts were then used to generate a set of consistent characters by our method and by each of the baselines. Next, these prompts were used to generate these characters in a predefined collection of novel contexts from the following list: • “a photo of [v] at the beach” • “a photo of [v] in the jungle” • “a photo of [v] in the snow” • “a photo of [v] in the street” • “a photo of [v] with a city in the background” • “a photo of [v] with a mountain in the background” • “a photo of [v] with the Eiffel Tower in the background” • “a photo of [v] near the Statue of Liberty” • “a photo of [v] near the Sydney Opera House” • “a photo of [v] floating on top of water” • “a photo of [v] eating a burger” • “a photo of [v] drinking a beer” • “a photo of [v] wearing a blue hat” • “a photo of [v] wearing sunglasses” • “a photo of [v] playing with a ball” • “a photo of [v] as a police officer” where [v] is the newly-added token that represents the consistent character. B.3. User Study Details As explained in Section 4.2, we conducted a user study to evaluate our method, using the Amazon Mechanical Turk (AMT) platform [2]. We used the same generated prompts and samples that were used in Section 4.1, and asked the evaluators to rate the prompt similarity and identity consistency of each result on a Likert scale of 1–5. For ranking the prompt similarity, the evaluators were instructed the following: “For each of the following images, please rank on a scale of 1 to 5 its correspondence to this text description: {PROMPT}. The character in the image can be anything (e.g., a person, an animal, a toy etc.” where {PROMPT} is the target text prompt (in which we replaced the special token with the word “character”). All the baselines, as well as our method, were presented in the same page, and the evaluators were asked to rate each one of the results using a slider from 1 (“Do not match at all”) to 5 (“Match perfectly”). Next, to assess identity consistency, we took for each one of the characters two generated images that correspond to different target text prompts, put them next to each other, and instructed the evaluators the following: “For each of the following image pairs, please rank on a scale of 1 to 5 if they contain the same character (1 means that they contain totally different characters and 5 means that they contain exactly the same character). The images can have different backgrounds”. We put all the compared images on the same page, and the evaluators were asked to rate each one of the pairs using a slider from 1 (“Totally different characters”) to 5 (“Exactly the same character”). We collected three ratings per question, resulting in 1104 ratings per task (prompt similarity and identity consistency). The time allotted per task was one hour, to allow the raters to properly evaluate the results without time pressure. The means and variances of the user study responses are reported in Table 1. In addition, we conducted a statistical analysis of our user study by validating that the difference between all the conditions is statistically significant using Kruskal-Wallis [40] test (p < 1e−28 for the text similarity test and p < 1e−76 for the identity consistency text). Lastly, we used Tukey’s honestly significant difference procedure [83] to show that the comparison of our method against all the baselines is statistically significant, as detailed in Table 2. B.4. Applications Implementation Details In Section 4.4, we presented three downstream applications of our method. Story illustration. Given a long story, e.g., “This is a story about Jasper, a cute mink with a brown jacket and red pants. Jasper started his day by jogging on the beach, and afterwards, he enjoyed a coffee meetup with a friend in the heart of New York City. As the day drew to a close, he settled into his cozy apartment to review a paper”, one can create a consistent character from the main character description (“a cute mink with a brown jacket and red pants”), then they can generate the various scenes by simply rephrasing the sentence: “[v] jogging on the beach”


“[v] drinking coffee with his friend in the heart of New York City”


“[v] reviewing a paper in his cozy apartment” Local image editing. Our method can be simply integrated with Blended Latent Diffusion [5, 7] for editing images locally: given a text prompt, we start by running our method to extract a consistent identity, then, given an input image and mask, we can plant the character in the image within the mask boundaries. In addition, we can provide a local text description for the character. Additional pose control. Our method can be integrated with ControlNet [97]: given a text prompt, we first apply our method to extract a consistent identity Θ = (θ, τ ), where θ are the LoRA weights and τ is a set of custom text embeddings. Then, we can take an off-the-shelf pre-trained ControlNet model, plug-in our representation Θ, and use it to generate the consistent character in different poses given by the user. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Omri Avrahami, Google Research and The Hebrew University of Jerusalem; (2) Amir Hertz, Google Research; (3) Yael Vinker, Google Research and Tel Aviv University; (4) Moab Arar, Google Research and Tel Aviv University; (5) Shlomi Fruchter, Google Research; (6) Ohad Fried, Reichman University; (7) Daniel Cohen-Or, Google Research and Tel Aviv University; (8) Dani Lischinski, Google Research and The Hebrew University of Jerusalem. Authors: Authors: (1) Omri Avrahami, Google Research and The Hebrew University of Jerusalem; (2) Amir Hertz, Google Research; (3) Yael Vinker, Google Research and Tel Aviv University; (4) Moab Arar, Google Research and Tel Aviv University; (5) Shlomi Fruchter, Google Research; (6) Ohad Fried, Reichman University; (7) Daniel Cohen-Or, Google Research and Tel Aviv University; (8) Dani Lischinski, Google Research and The Hebrew University of Jerusalem. Table of Links Abstract and Introduction Related Work Method Experiments Limitations and Conclusions A. Additional Experiments B. Implementation Details C. Societal Impact References Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experiments Experiments Limitations and Conclusions Limitations and Conclusions A. Additional Experiments A. Additional Experiments B. Implementation Details B. Implementation Details C. Societal Impact C. Societal Impact References References B. Implementation Details In this section, we provide the implementation details that were omitted from the main paper. In Appendix B.1 we provide the implementation details of our method and the baselines. Then, in Appendix B.2 we provide the implementation details of the automatic metrics that we used to evaluate our method against the baselines. In Appendix B.3 we provide the implementation details and the statistical analysis for the user study we conducted. Lastly, in Appendix B.4 we provide the implementation details for the applications we presented. B.1. Method Implementation Details We based our method, and all the baselines (except ELITE [90] and BLIP-diffusion [42]) on Stable Diffusion XL (SDXL) [57], which is the state-of-the-art open source text-to-image model, at the writing of this paper. We used the official ELITE implementation, that uses Stable Diffusion V1.4, and the official implementation of BLIPdiffusion, that uses Stable Diffusion V1.5. We could not replace these two baselines to SDXL backbone, as the encoders were trained on these specific models. As for the rest of the baselines, we used the same SDXL architecture and weights. List of the third-party packages that we used: • Official SDXL [57] implementation by HuggingFace Diffusers [86] at https : / / github . com / huggingface/diffusers • Official SDXL LoRA DB implementation by HuggingFace Diffusers [86] at https : / / github . com / huggingface/diffusers. • Official ELITE [90] implementation at https : / / github.com/csyxwei/ELITE • Official BLIP-diffusion [42] implementation at https: / / github . com / salesforce / LAVIS / tree / main/projects/blip-diffusion • Official IP-adapter [93] implementation at https:// github.com/tencent-ailab/IP-Adapter • DINOv2 [54] ViT-g/14, DINOv1 [14] ViT-B/16 and CLIP [61] ViT-L/14 implementation by HuggingFace Transformers [91] at https : / / github . com / huggingface/transformers B.2. Automatic Metrics Implementation Details In order to automatically evaluate our method and the baselines quantitatively, we instructed ChatGPT [53] to generate prompts for characters of different types (e.g., animals, creatures, objects, etc.) in different styles (e.g., stickers, animations, photorealistic images, etc.). These prompts were then used to generate a set of consistent characters by our method and by each of the baselines. Next, these prompts were used to generate these characters in a predefined collection of novel contexts from the following list: • “a photo of [v] at the beach” • “a photo of [v] in the jungle” • “a photo of [v] in the snow” • “a photo of [v] in the street” • “a photo of [v] with a city in the background” • “a photo of [v] with a mountain in the background” • “a photo of [v] with the Eiffel Tower in the background” • “a photo of [v] near the Statue of Liberty” • “a photo of [v] near the Sydney Opera House” • “a photo of [v] floating on top of water” • “a photo of [v] eating a burger” • “a photo of [v] drinking a beer” • “a photo of [v] wearing a blue hat” • “a photo of [v] wearing sunglasses” • “a photo of [v] playing with a ball” • “a photo of [v] as a police officer” where [v] is the newly-added token that represents the consistent character. B.3. User Study Details As explained in Section 4.2, we conducted a user study to evaluate our method, using the Amazon Mechanical Turk (AMT) platform [2]. We used the same generated prompts and samples that were used in Section 4.1, and asked the evaluators to rate the prompt similarity and identity consistency of each result on a Likert scale of 1–5. For ranking the prompt similarity, the evaluators were instructed the following: “For each of the following images, please rank on a scale of 1 to 5 its correspondence to this text description: {PROMPT}. The character in the image can be anything (e.g., a person, an animal, a toy etc.” where {PROMPT} is the target text prompt (in which we replaced the special token with the word “character”). All the baselines, as well as our method, were presented in the same page, and the evaluators were asked to rate each one of the results using a slider from 1 (“Do not match at all”) to 5 (“Match perfectly”). Next, to assess identity consistency, we took for each one of the characters two generated images that correspond to different target text prompts, put them next to each other, and instructed the evaluators the following: “For each of the following image pairs, please rank on a scale of 1 to 5 if they contain the same character (1 means that they contain totally different characters and 5 means that they contain exactly the same character). The images can have different backgrounds”. We put all the compared images on the same page, and the evaluators were asked to rate each one of the pairs using a slider from 1 (“Totally different characters”) to 5 (“Exactly the same character”). We collected three ratings per question, resulting in 1104 ratings per task (prompt similarity and identity consistency). The time allotted per task was one hour, to allow the raters to properly evaluate the results without time pressure. The means and variances of the user study responses are reported in Table 1. In addition, we conducted a statistical analysis of our user study by validating that the difference between all the conditions is statistically significant using Kruskal-Wallis [40] test (p < 1e−28 for the text similarity test and p < 1e−76 for the identity consistency text). Lastly, we used Tukey’s honestly significant difference procedure [83] to show that the comparison of our method against all the baselines is statistically significant, as detailed in Table 2. B.4. Applications Implementation Details In Section 4.4, we presented three downstream applications of our method. Story illustration. Given a long story, e.g., “This is a story about Jasper, a cute mink with a brown jacket and red pants. Jasper started his day by jogging on the beach, and afterwards, he enjoyed a coffee meetup with a friend in the heart of New York City. As the day drew to a close, he settled into his cozy apartment to review a paper” , one can create a consistent character from the main character description (“a cute mink with a brown jacket and red pants”) , then they can generate the various scenes by simply rephrasing the sentence: Story illustration. “This is a story about Jasper, a cute mink with a brown jacket and red pants. Jasper started his day by jogging on the beach, and afterwards, he enjoyed a coffee meetup with a friend in the heart of New York City. As the day drew to a close, he settled into his cozy apartment to review a paper” (“a cute mink with a brown jacket and red pants”) “[v] jogging on the beach” “[v] drinking coffee with his friend in the heart of New York City” “[v] reviewing a paper in his cozy apartment” “[v] jogging on the beach” “[v] jogging on the beach” “[v] jogging on the beach” “[v] drinking coffee with his friend in the heart of New York City” “[v] drinking coffee with his friend in the heart of New York City” “[v] drinking coffee with his friend in the heart of New York City” “[v] reviewing a paper in his cozy apartment” “[v] reviewing a paper in his cozy apartment” “[v] reviewing a paper in his cozy apartment” Local image editing. Our method can be simply integrated with Blended Latent Diffusion [5, 7] for editing images locally: given a text prompt, we start by running our method to extract a consistent identity, then, given an input image and mask, we can plant the character in the image within the mask boundaries. In addition, we can provide a local text description for the character. Local image editing. Additional pose control. Our method can be integrated with ControlNet [97]: given a text prompt, we first apply our method to extract a consistent identity Θ = (θ, τ ), where θ are the LoRA weights and τ is a set of custom text embeddings. Then, we can take an off-the-shelf pre-trained ControlNet model, plug-in our representation Θ, and use it to generate the consistent character in different poses given by the user. Our method can be integrated with ControlNet [97]: given a text prompt, This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

The Chosen One: Consistent Characters in Text-to-Image Diffusion Models: Implementation Details

Have you heard about the latest sensation in the generative AI world, the Luma Dream Machine? It’s being called OpenAI’s Sora’s biggest rival. But is it really that good? Comparing them is tricky because Dream Machine is available to everyone, while Sora isn’t. But let’s see what we can find out. Hard to deny that right now, Dream Machine is leading because we can actually use it. It’s the best tool for generating videos from images, beating competitors like Pika and Runway ML. But how does it compare to the mysterious Sora? Since we can’t use Sora, we’ll compare OpenAI’s public demos to what Luma Dream Machine can do. Here’s the plan: we’ll take the first frame from OpenAI’s demo videos and use the same prompt with Luma’s Dream Machine. This will show us how well Dream Machine can copy the same physics, movement, and space as Sora. Even if OpenAI’s demos are cherry-picked, we can still compare the details and see how both models perform. Below, I’ve put together some video comparisons. Each set has three examples. The first video is from OpenAI’s demo on Sora’s website. The second is made with Dream Machine’s image-to-video feature, using the same prompt and the first frame of Sora’s demo as a guide. The third shows how Luma’s tool works with just the prompt. This is interesting because both Sora and Dream Machine use text-to-video, so we can compare their creativity and how well they follow the prompt. So, without further ado, let’s check out the examples and see which tool comes out on top. Tokyo Walk https://youtu.be/-1sqHhXZHjM?embedable=true Let’s compare OpenAI’s demo with the Luma Dream Machine. In the first comparison, Dream Machine shows impressive camera movement, and the main person’s actions are smooth and natural. However, there are issues with unnatural artifacts and inconsistent looks of objects and people throughout the clip. Unlike OpenAI’s video, the background crowd appears to melt and change shape as the video progresses. The main character’s face also changes unnaturally, making the video look obviously fake, a problem Sora doesn’t have. In the text-to-video example, Dream Machine’s video isn’t bad, but the unnatural morphing of objects is noticeable. For instance, an umbrella appears in a pedestrian’s hand out of nowhere, clearly indicating AI generation. This makes it no competition for royalty-free stock clips. A thing that Sora’s generations can probably be. However, Dream Machine sticks to the prompt well: black jacket, red dress, lipstick, sunglasses, reflective street, pedestrians, and neon lights are all present. So, well done on following the details! Gold Rush https://youtu.be/CXHwkkCdUCE?embedable=true When comparing Luma’s image-to-video result to OpenAI’s, it’s not terrible. However, the camera movement isn’t as smooth as in the Tokyo video, stopping abruptly and making the scene harsh. The worst part is the character’s movement at the end of the clip, which appears unnatural and random. Additionally, the buildings on the left degrade in realism with each frame, a problem not seen in Sora’s example. Similar to the previous clip, there’s a lack of stability and consistency, with too many artifacts. Sora also excels in making the clip look vintage with a low frame rate and overall old-school quality, suggesting it can stylize its output according to the prompt, which Dream Machine didn’t achieve here. In the text-to-video example with a short and open-ended prompt, Luma’s model chose a different scene from gold rush history. It seems more in style with the era, using the right colors and lighting. However, the morphing effect and unnatural movement ruin the whole clip, making it unusable in video projects. SUV in the Dust https://youtu.be/6BkApqdQ5ZY?embedable=true This video is my favorite on OpenAI’s website. The car moves very naturally, with excellent lighting, shadows, and dynamics. It’s indistinguishable from a real video, making it perfect for content creators. In contrast, Dream Machine’s camera movement is correct, but the objects get squashed and mangled unnaturally. In the second part of the clip, the perspective becomes heavily distorted, clearly looking like an AI generation. For the text-to-video example, the result is actually quite nice—one of the best I’ve managed to get from Luma’s product. It’s less dynamic than the first one but looks pretty natural. However, it suffers from a different issue. The prompt was extensive, specifying that the SUV should be seen from behind with dust coming off the tires. Dream Machine interpreted it differently. This highlights a key aspect of AI content generators: without precise prompt interpretation, we can waste hours generating variations that don’t fit our vision or needs. Museum https://youtu.be/EBAyuJapaAY?embedable=true The Museum example is a different kind of beast. Well, not actually a beast - it’s more subtle, calm, and less dynamic. Just a simple walk with a steady camera. OpenAI’s version is accurate. It’s not exciting, but it doesn’t lack realism. Luma’s version presents a different camera movement but looks good too, without the distortions seen in other clips. The main issue is that the pictures that are not part of the original image appear blurred and lack definition. Overall, the video is fine, and with a few tweaks, we could get a proper result. There are no obvious visual flaws in the second video either. The gallery looks fine. My biggest issue is the choice of camera movement in the first part, which isn’t very realistic. Interestingly, Dream Machine generated two scenes for one prompt, with a cut in the middle showing a different room in the museum. It’s fascinating that the model decided to do this. The second part has better camera movement, making it more pleasing to the eye. Backward Jogger https://youtu.be/Ti_vdFi7Z3c?embedable=true This example is interesting because, on Sora’s page, it’s shown as one of the model’s problems: the jogger is running the wrong way. No treadmill works like that, but in the AI world, anything is possible. Is this Dream Machine’s chance to shine? The image-to-video result is actually pretty good. The jogger still runs backward, as in the input image, but the camera movement and jogger’s behavior are almost perfect. There are some minor distortions, and the camera perspective gets a little weird over time, but with a bit of cherry-picking, we could get a decent result for our productions. The version generated with just the prompt is also interesting. It’s very dynamic and a bit distorted, but this might suit certain productions, especially if a shaky, sketch-like aesthetic is desired. Not bad at all. Finally, Luma’s model is getting closer to its future competitor. Italian Puppy https://youtu.be/6lB5YLwe4jQ?embedable=true The last main example on the OpenAI site features a Dalmatian in a colorful Italian city. The original video made with Sora isn’t perfect. In a longer clip, the dog starts acting a bit oddly, and its animation isn’t as natural as in other showcased videos. How does Luma’s newest AI handle this? Not well at all. Maybe it’s because they only had one take (and the generator is pretty rate-limited), but what we see is a festival of glitches and unrealistic imagery. The dog’s texture changes as the video progresses, the buildings look like they’re made of playdough, and another dog-like abomination appears at the end, making it look more like Salvador Dali’s work than a real video. This is definitely the worst example so far. Dream Machine’s own creation isn’t any better. It didn’t follow the prompt, failing to include the Dalmatian at all. There’s no window for the dog to sit in, the buildings look cartoonish, and the overall architecture is nonsensical. Worst of all are the cyclists on heavily distorted bikes, deformed creatures driving into the canal, or morphing into other cyclists without any reason. This falls way below expectations. Verdict? For what’s available to the public now, Luma’s new AI is truly impressive. It pushes the boundaries, generating really nice camera motion and often very realistic movements of people and objects. It seems to work better when provided with a reference image, producing effects better than its current competition. But is it as good as Sora? It seems far from it, at least for now. Sora’s creations can be mistaken for real videos, at least at first glance. The showcase suggests that Sora could compete with stock videos and make life easier for filmmakers and content creators. Dream Machine, on the other hand, often produces glitches and doesn’t always follow prompts accurately. It’s another step forward in model improvements, but still not reliable and stable enough for widespread use. Is it a true rival for Sora? Not yet. However, we haven’t interacted directly with Sora, and OpenAI’s showcase might be carefully curated. Sora could potentially make similar mistakes as Luma’s model. Until Sora is publicly available, we can’t be certain. Personally, I’m glad we have Dream Machine. It brings us closer to the perfect AI video generator. It’s useful in some cases and will likely improve over time. I appreciate Luma releasing this tool, giving us another way to enjoy generative AI for video clips. On the other hand, I hope Sora works as shown in the showcase. If it does, it will be a significant leap forward. I’m eagerly waiting for it to become publicly available so I can compare the results myself. Have you heard about the latest sensation in the generative AI world, the Luma Dream Machine? It’s being called OpenAI’s Sora’s biggest rival. But is it really that good? Comparing them is tricky because Dream Machine is available to everyone, while Sora isn’t. But let’s see what we can find out. Hard to deny that right now, Dream Machine is leading because we can actually use it. It’s the best tool for generating videos from images, beating competitors like Pika and Runway ML. But how does it compare to the mysterious Sora? Since we can’t use Sora, we’ll compare OpenAI’s public demos to what Luma Dream Machine can do. Here’s the plan: we’ll take the first frame from OpenAI’s demo videos and use the same prompt with Luma’s Dream Machine. This will show us how well Dream Machine can copy the same physics, movement, and space as Sora. Even if OpenAI’s demos are cherry-picked, we can still compare the details and see how both models perform. Below, I’ve put together some video comparisons. Each set has three examples. The first video is from OpenAI’s demo on Sora’s website. The second is made with Dream Machine’s image-to-video feature, using the same prompt and the first frame of Sora’s demo as a guide. The third shows how Luma’s tool works with just the prompt. This is interesting because both Sora and Dream Machine use text-to-video, so we can compare their creativity and how well they follow the prompt. So, without further ado, let’s check out the examples and see which tool comes out on top. Tokyo Walk https://youtu.be/-1sqHhXZHjM?embedable=true https://youtu.be/-1sqHhXZHjM?embedable=true Let’s compare OpenAI’s demo with the Luma Dream Machine. In the first comparison, Dream Machine shows impressive camera movement, and the main person’s actions are smooth and natural. However, there are issues with unnatural artifacts and inconsistent looks of objects and people throughout the clip. Unlike OpenAI’s video, the background crowd appears to melt and change shape as the video progresses. The main character’s face also changes unnaturally, making the video look obviously fake, a problem Sora doesn’t have. In the text-to-video example, Dream Machine’s video isn’t bad, but the unnatural morphing of objects is noticeable. For instance, an umbrella appears in a pedestrian’s hand out of nowhere, clearly indicating AI generation. This makes it no competition for royalty-free stock clips. A thing that Sora’s generations can probably be. However, Dream Machine sticks to the prompt well: black jacket, red dress, lipstick, sunglasses, reflective street, pedestrians, and neon lights are all present. So, well done on following the details! Gold Rush https://youtu.be/CXHwkkCdUCE?embedable=true https://youtu.be/CXHwkkCdUCE?embedable=true When comparing Luma’s image-to-video result to OpenAI’s, it’s not terrible. However, the camera movement isn’t as smooth as in the Tokyo video, stopping abruptly and making the scene harsh. The worst part is the character’s movement at the end of the clip, which appears unnatural and random. Additionally, the buildings on the left degrade in realism with each frame, a problem not seen in Sora’s example. Similar to the previous clip, there’s a lack of stability and consistency, with too many artifacts. Sora also excels in making the clip look vintage with a low frame rate and overall old-school quality, suggesting it can stylize its output according to the prompt, which Dream Machine didn’t achieve here. In the text-to-video example with a short and open-ended prompt, Luma’s model chose a different scene from gold rush history. It seems more in style with the era, using the right colors and lighting. However, the morphing effect and unnatural movement ruin the whole clip, making it unusable in video projects. SUV in the Dust https://youtu.be/6BkApqdQ5ZY?embedable=true https://youtu.be/6BkApqdQ5ZY?embedable=true This video is my favorite on OpenAI’s website. The car moves very naturally, with excellent lighting, shadows, and dynamics. It’s indistinguishable from a real video, making it perfect for content creators. In contrast, Dream Machine’s camera movement is correct, but the objects get squashed and mangled unnaturally. In the second part of the clip, the perspective becomes heavily distorted, clearly looking like an AI generation. For the text-to-video example, the result is actually quite nice—one of the best I’ve managed to get from Luma’s product. It’s less dynamic than the first one but looks pretty natural. However, it suffers from a different issue. The prompt was extensive, specifying that the SUV should be seen from behind with dust coming off the tires. Dream Machine interpreted it differently. This highlights a key aspect of AI content generators: without precise prompt interpretation, we can waste hours generating variations that don’t fit our vision or needs. Museum https://youtu.be/EBAyuJapaAY?embedable=true https://youtu.be/EBAyuJapaAY?embedable=true The Museum example is a different kind of beast. Well, not actually a beast - it’s more subtle, calm, and less dynamic. Just a simple walk with a steady camera. OpenAI’s version is accurate. It’s not exciting, but it doesn’t lack realism. Luma’s version presents a different camera movement but looks good too, without the distortions seen in other clips. The main issue is that the pictures that are not part of the original image appear blurred and lack definition. Overall, the video is fine, and with a few tweaks, we could get a proper result. There are no obvious visual flaws in the second video either. The gallery looks fine. My biggest issue is the choice of camera movement in the first part, which isn’t very realistic. Interestingly, Dream Machine generated two scenes for one prompt, with a cut in the middle showing a different room in the museum. It’s fascinating that the model decided to do this. The second part has better camera movement, making it more pleasing to the eye. Backward Jogger https://youtu.be/Ti_vdFi7Z3c?embedable=true https://youtu.be/Ti_vdFi7Z3c?embedable=true This example is interesting because, on Sora’s page, it’s shown as one of the model’s problems: the jogger is running the wrong way. No treadmill works like that, but in the AI world, anything is possible. Is this Dream Machine’s chance to shine? The image-to-video result is actually pretty good. The jogger still runs backward, as in the input image, but the camera movement and jogger’s behavior are almost perfect. There are some minor distortions, and the camera perspective gets a little weird over time, but with a bit of cherry-picking, we could get a decent result for our productions. The version generated with just the prompt is also interesting. It’s very dynamic and a bit distorted, but this might suit certain productions, especially if a shaky, sketch-like aesthetic is desired. Not bad at all. Finally, Luma’s model is getting closer to its future competitor. Italian Puppy https://youtu.be/6lB5YLwe4jQ?embedable=true https://youtu.be/6lB5YLwe4jQ?embedable=true The last main example on the OpenAI site features a Dalmatian in a colorful Italian city. The original video made with Sora isn’t perfect. In a longer clip, the dog starts acting a bit oddly, and its animation isn’t as natural as in other showcased videos. How does Luma’s newest AI handle this? Not well at all. Maybe it’s because they only had one take (and the generator is pretty rate-limited), but what we see is a festival of glitches and unrealistic imagery. The dog’s texture changes as the video progresses, the buildings look like they’re made of playdough, and another dog-like abomination appears at the end, making it look more like Salvador Dali’s work than a real video. This is definitely the worst example so far. Dream Machine’s own creation isn’t any better. It didn’t follow the prompt, failing to include the Dalmatian at all. There’s no window for the dog to sit in, the buildings look cartoonish, and the overall architecture is nonsensical. Worst of all are the cyclists on heavily distorted bikes, deformed creatures driving into the canal, or morphing into other cyclists without any reason. This falls way below expectations. Verdict? For what’s available to the public now, Luma’s new AI is truly impressive. It pushes the boundaries, generating really nice camera motion and often very realistic movements of people and objects. It seems to work better when provided with a reference image, producing effects better than its current competition. But is it as good as Sora? It seems far from it, at least for now. Sora’s creations can be mistaken for real videos, at least at first glance. The showcase suggests that Sora could compete with stock videos and make life easier for filmmakers and content creators. Dream Machine, on the other hand, often produces glitches and doesn’t always follow prompts accurately. It’s another step forward in model improvements, but still not reliable and stable enough for widespread use. Is it a true rival for Sora? Not yet. However, we haven’t interacted directly with Sora, and OpenAI’s showcase might be carefully curated. Sora could potentially make similar mistakes as Luma’s model. Until Sora is publicly available, we can’t be certain. Personally, I’m glad we have Dream Machine. It brings us closer to the perfect AI video generator. It’s useful in some cases and will likely improve over time. I appreciate Luma releasing this tool, giving us another way to enjoy generative AI for video clips. On the other hand, I hope Sora works as shown in the showcase. If it does, it will be a significant leap forward. I’m eagerly waiting for it to become publicly available so I can compare the results myself.

Is OpenAI's Sora in Trouble Yet?

ওপেনএআই এর সোরা কি এখনও সমস্যায় আছে?

Steckt Sora von OpenAI bereits in Schwierigkeiten?

¿Sora de OpenAI ya está en problemas?

Sora d'OpenAI est-il déjà en difficulté ?

क्या ओपनएआई का सोरा अभी भी संकट में है?

OpenAI の Sora はすでに問題を抱えているのか?

OpenAI의 Sora는 아직 문제가 있습니까?

O Sora da OpenAI ainda está em apuros?

Сора OpenAI уже в беде?

OpenAI Sora'nın Başı Hala Sorunda mı?

Sora của OpenAI có gặp rắc rối không?

OpenAI 的 Sora 遇到麻烦了吗？

Authors:
(1) Kedan Li, University of Illinois at Urbana-Champaign;
(2) Min Jin Chong, University of Illinois at Urbana-Champaign;
(3) Jingen Liu, JD AI Research;
(4) David Forsyth, University of Illinois at Urbana-Champaign. Table of Links Abstract and Intro
Related Work
Proposed Method
Experiments
Conclusions and References 2. Related Work Image synthesis: Spatial transformer networks estimate geometric transformations using neural networks [23]. Subsequent work [28,39] shows how to warp one object to another. Warping can be used to produce images of rigid objects [26,30] and non-rigid objects (e.g., clothing) [17,12,45]. In contrast to prior work, we use multiple spatial warpers. Our warps must be combined into a single image, and our U-Net for producing this image follows trends in inpainting (methods that fill in missing portions of an image, see [48,31,50,49]). Han et al. [16,52] show inpainting methods can complete missing clothing items on people. In our work, we use FID∞ to quantitatively evaluate our method. This is based on the Frchet Inception Distance (FID) [18], a common metric in generative image modelling [5,54,29]. Chong et al. [9] recently showed that FID is biased; extrapolation removes the bias, to an unbiased score (FID∞). Generating clothed people: Zhu et al. [57] used a conditional GAN to generate images based on pose skeleton and text descriptions of garment. SwapNet [38] learns to transfer clothes from person A to person B by disentangling clothing and pose features. Hsiao et al. [20] learned a fashion model synthesis network using per-garment encodings to enable convenient minimal edit to specific items. In contrast, we warp products onto real model images. Shape matching underlies our method to match product to model. Tsiao et al. [19] built a shape embedding to enable matching between human body and well-fitting clothing items. Prior work estimated the shape of human body [4,27], clothing items [10,25] and both [35,40], through 2D images. The DensePose [1] descriptor helps modeling the deformation and shading of cloth and, therefore, has been adopted by recent work [36,13,47,51,7,52]. Virtual try-on (VTO) maps a product to a model image. VITON [17] uses a U-Net to generate a coarse synthesis and a mask on the model where the product is presented. A mapping from the product mask to the on-model mask is learned through Thin plate spline (TPS) transformation [3]. The learned mapping is applied on the product image to create a warp. Following their work, Wang et al. [45] improved the architecture using a Geometric Matching Module [39] to estimate the TPS transformations parameters directly from pairs of product image and target person. They train a separate refinement network to combine the warp and the target image. VTNFP [53] extends the work by incorporatiing body segments prediction and later works follow similar procedure [37,24,42,22,2]. However, TPS transformation fails to produce reasonable warps, due to the noisiness of generated masks in our dataset, as shown in Figure 6 right. Instead, we adopt affine transformations which we have found to be more robust to imperfections instead of TPS transformation. A group of following work extended the task to multi-pose. Warping-GAN [11] combined adversarial training with GMM, and generate post and texture separately using a two stage network. MG-VTON [12] further refine the generation method using a three-stage generation network. Other work [21,55,51,7,46] followed similar procedure. Han et al. [15] argued that TPS transformation has low degree of freedom and proposed a flow-based method to create the warp. Much existing virtual try-on work [17,12,21,47,55,53,24,37] is evaluated on datasets that only have tops (t-shirt, shirt, etc.). Having only tops largely reduces the likelihood of shape mismatch as tops have simple and similar shapes. In our work, we extend the problem to include clothing items of all categories(tshirt, shirt, pants, shorts, dress, skirt, robe, jacket, coat, etc.), and propose a method for matching the shape between the source product and the target model. Evaluation shows that using pairs that match in shape significantly increases the generation quality for both our and prior work (table 4.3). In addition, real studio outfits are often covered by an unzipped/unbuttoned outerwear, which is also not presented in prior work [17,12,21,47,55,53,37]. This can cause partition or severe occlusion to the garment, and is not addressed by prior work as shown in Figure 6. We show that our multi-warp generation module ameliorates these difficulties. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Kedan Li, University of Illinois at Urbana-Champaign; (2) Min Jin Chong, University of Illinois at Urbana-Champaign; (3) Jingen Liu, JD AI Research; (4) David Forsyth, University of Illinois at Urbana-Champaign. Authors: Authors: (1) Kedan Li, University of Illinois at Urbana-Champaign; (2) Min Jin Chong, University of Illinois at Urbana-Champaign; (3) Jingen Liu, JD AI Research; (4) David Forsyth, University of Illinois at Urbana-Champaign. Table of Links Abstract and Intro Related Work Proposed Method Experiments Conclusions and References Abstract and Intro Abstract and Intro Related Work Related Work Proposed Method Proposed Method Experiments Experiments Conclusions and References Conclusions and References 2. Related Work Image synthesis : Spatial transformer networks estimate geometric transformations using neural networks [23]. Subsequent work [28,39] shows how to warp one object to another. Warping can be used to produce images of rigid objects [26,30] and non-rigid objects (e.g., clothing) [17,12,45]. In contrast to prior work, we use multiple spatial warpers. Image synthesis Our warps must be combined into a single image, and our U-Net for producing this image follows trends in inpainting (methods that fill in missing portions of an image, see [48,31,50,49]). Han et al. [16,52] show inpainting methods can complete missing clothing items on people. In our work, we use FID∞ to quantitatively evaluate our method. This is based on the Frchet Inception Distance (FID) [18], a common metric in generative image modelling [5,54,29]. Chong et al. [9] recently showed that FID is biased; extrapolation removes the bias, to an unbiased score (FID∞). Generating clothed people: Zhu et al. [57] used a conditional GAN to generate images based on pose skeleton and text descriptions of garment. SwapNet [38] learns to transfer clothes from person A to person B by disentangling clothing and pose features. Hsiao et al. [20] learned a fashion model synthesis network using per-garment encodings to enable convenient minimal edit to specific items. In contrast, we warp products onto real model images. Generating clothed people: Shape matching underlies our method to match product to model. Tsiao et al. [19] built a shape embedding to enable matching between human body and well-fitting clothing items. Prior work estimated the shape of human body [4,27], clothing items [10,25] and both [35,40], through 2D images. The DensePose [1] descriptor helps modeling the deformation and shading of cloth and, therefore, has been adopted by recent work [36,13,47,51,7,52]. Shape matching Virtual try-on (VTO) maps a product to a model image. VITON [17] uses a U-Net to generate a coarse synthesis and a mask on the model where the product is presented. A mapping from the product mask to the on-model mask is learned through Thin plate spline (TPS) transformation [3]. The learned mapping is applied on the product image to create a warp. Following their work, Wang et al. [45] improved the architecture using a Geometric Matching Module [39] to estimate the TPS transformations parameters directly from pairs of product image and target person. They train a separate refinement network to combine the warp and the target image. VTNFP [53] extends the work by incorporatiing body segments prediction and later works follow similar procedure [37,24,42,22,2]. However, TPS transformation fails to produce reasonable warps, due to the noisiness of generated masks in our dataset, as shown in Figure 6 right. Instead, we adopt affine transformations which we have found to be more robust to imperfections instead of TPS transformation. A group of following work extended the task to multi-pose. Warping-GAN [11] combined adversarial training with GMM, and generate post and texture separately using a two stage network. MG-VTON [12] further refine the generation method using a three-stage generation network. Other work [21,55,51,7,46] followed similar procedure. Han et al. [15] argued that TPS transformation has low degree of freedom and proposed a flow-based method to create the warp. Virtual try-on Much existing virtual try-on work [17,12,21,47,55,53,24,37] is evaluated on datasets that only have tops (t-shirt, shirt, etc.). Having only tops largely reduces the likelihood of shape mismatch as tops have simple and similar shapes. In our work, we extend the problem to include clothing items of all categories(tshirt, shirt, pants, shorts, dress, skirt, robe, jacket, coat, etc.), and propose a method for matching the shape between the source product and the target model. Evaluation shows that using pairs that match in shape significantly increases the generation quality for both our and prior work (table 4.3). In addition, real studio outfits are often covered by an unzipped/unbuttoned outerwear, which is also not presented in prior work [17,12,21,47,55,53,37]. This can cause partition or severe occlusion to the garment, and is not addressed by prior work as shown in Figure 6. We show that our multi-warp generation module ameliorates these difficulties. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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