As the Wall Street Journal reports, Nadhmi al-Nasr, the CEO of Saudi Arabia's futuristic city project Neom — which includes The Line, a planned pair of skycrapers that would be 100 miles in length — has abruptly departed the role he's held since 2018.

CEO in Charge of Saudi Arabia’s 100-Mile Skyscraper Out After Allegations of Mass Employee Death

The Jets will face the Blue Jackets on Friday night.

Line Combinations: Jets at Blue Jackets - Yahoo Sports

In the process of her reporting, the anonymous reporter discovers an extremely inconvenient truth: during the course of its multitrillion-dollar Vision 2030 project — which was launched in 2017 and includes its in-progress [right?] 100-mile skyscraper called "The Line" within its futuristic NEOM ...

A Staggering Number of Workers Have Reportedly Died While Building Saudi Arabia’s 100-Mile Skyscraper

Sameer Malhotra is cofounder and CEO of TrueFort, a former Wall Street tech exec and an expert in IT infrastructure and cybersecurity.

Council Post: Behavioral Baselining: The Last Line Of Defense In Cybersecurity

The aerospace giant said it could raise as much as $25 billion in debt or equity over the next three years, including a $10 billion line of credit.

Boeing Seeks to Line Up Billions in Financing as Strike Goes On - The New York Times

ABC Friday announced six additional Monday Night Football simulcasts to its previously announced schedule (see below for the full line-up). So what does this mean for its other already scheduled programming? Hidden camera series What Would You Do? will move to Wednesdays beginning Oct.

ABC Beefs Up Monday Night Football Line-Up — What’s on the Move? - Yahoo Sports

Line Next's web3 mini-games platform can be accessed directly from Line’s chat app, letting users connect wallets, play, and invite friends.

Line Next launching web3 mini-game platform to Line's 2 billion chat users - BlockchainGamerBiz

LINE 9.12.0 and higher for iOS and Android.

aimai to I like – LINE stickers | LINE STORE

Line

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Binance to launch crypto-fiat exchange in Uganda, Korea’s new Crypto regulation; Messaging App line to release an exchange; JD working with Huobi What Crypto insiders are reading on Asia. Subscribe now 🌟 Top News Binance is supporting the Ugandan Shilling, alongside major cryptocurrencies. about to launch a crypto-fiat exchange in Uganda, http://bit.ly/2tCsO7w Korea pertaining to AML and KYC requirements for crypto exchanges. It’s investigating into three major banks and having banks alert suspicious fund movements to avoid the “kimchi premium” from happening. has revealed a new crypto regulatory framework and guidelines http://bit.ly/2N5OhOg is including bitcoin, ethereum and litecoin, at a trading fee of 0.1%. The Japanese messaging app makes a big push into financial services to compete against WhatsApp and WeChat. Line launching its own exchange supporting 30 cryptocurrencies, https://on.ft.com/2ySsKWa JD most recently was supported by the JD.com’s cloud arm is working with Huobi to build blockchain-based technology and applications. Chinese gov’t to create asset backed tokens. 💰 Coin and Exchange News China’s in a move towards European market. Huobi opens London office http://bit.ly/2tCrscU after collaborating with worldwide exchanges, and it has also disclosed the accounts and crypto assets that were stolen during the breach. Hacked Bithumbit has now reduced its stolen damage by almost half http://bit.ly/2tMv0IL the first gaming platform and the first ICO to launch on VeChainThor platform, is having a live AMA. Plair, http://bit.ly/2tCqgX0 21 nodes is too few to deter cartels and currently nodes are dominated by Bitfinex, Huobi, Chinese, Koreas. Redditor claim EOS’s http://bit.ly/2tBUU2L 💼 Business and Regulation News to develop a cross-border trade finance system that is built with distributed ledger technology. Hong Kong’s de facto central bank is planning to work Abu Dhabi http://bit.ly/2tAzW4c Blockchain can legally authenticate evidence, Chinese judge rules. http://bit.ly/2tJU96R CEO of SBI, one of Japan’s financial giants and longtime Ripple partner in Asia, talked about his belief in blockchain to get Japan’s economy back on track to regain the global position it once held. http://bit.ly/2tCJJGT for crypto-related activities, including any capital transaction that relates to distributed ledger accounts, blockchain, cryptocurrency and its derivatives, ICOs and exchange operations. China Banking Regulatory Commission researchers are advocating that domestic regulators issue licenses http://bit.ly/2ySAxDw ❗ Hey there Did I miss anything big today? Share with me the link, if you saw anything interesting. If I’m swayed, I’ll include your bit tomorrow (with attribution)! 🌹🌹🌹 Check our past 3 weekly pieces on , , and Binance Bitmain China’s Crypto winners I’m conflicted about the news that Facebook may potentially acquire Coinbase, it reinforces centralization but then also bring legitimacy to the space. Perhaps the latter is more important right now.

Today’s Crypto News on Asia — Jun 28th

Gamification and the future of culture. Memecoins are cryptocurrency tokens inspired by internet memes. By now, you’ve probably heard of $DOGE coin, the first-ever memecoin launched in 2013 as a joke about Bitcoin. Billy Markus, the co-creator of Doge, sold his holdings for a modest $10,000. Today, the market cap of DOGE is $52 billion. If Billy had held onto his DOGE coins, they’d be worth almost $20 million today. Dare I say, memecoins are here to stay. There are probably hundreds of thousands of unique memecoins in existence today — some have gone to zero, others are dormant, some are still in flux, and a few are seeing steady growth. I have done freelance work in the crypto industry since 2018, but I never fully ventured into memecoins. I thought Bitcoin, a more established cryptocurrency, was volatile enough. When you’re just getting used to the concept of volatile markets and digital, invisible money, diving into something even less established, more volatile, and based on a meme seemed reckless. Why invest in a joke? At the time, it all seemed shady. It took me a few years to truly understand the appeal of memecoins. The surge in Bitcoin that followed Donald Trump’s win in the 2024 presidential election gave me perspective. BTC hit $67,000 on November 5th, 2024. After Trump, a self-proclaimed pro-Bitcoin president, was declared the winner, the price shot up over 39%, currently sitting at $94,000. This is an insane amount of growth — a new all-time high for Bitcoin. As the crypto surge unfolded, I noticed that memecoins were surging alongside Bitcoin. Google trends for crypto-related searches were going up, and people were claiming massive 300x returns on memecoin investments — truly life-changing money. The prices of these alternative coins are incredibly volatile. For example, take $PNUT, which is based on an internet-famous squirrel that was confiscated and euthanized by the New York government. Its price has shot up 1692% since November 4th, after Elon Musk tweeted about the late squirrel, saying, “America was saved by a squirrel and a meme coin!” Of course, the tweet boosted the price even further. I decided to buy some Solana since many memecoins use the Solana blockchain due to its fast transaction speeds. Hundreds of coins are created every minute on the Solana ecosystem. You can easily and quickly send and receive Solana to buy many popular and rising memecoins. I traded, made some money, and lost some too. But I learned something that, perhaps, many already in the trenches of memecoin trading have long known: there is an insane amount of money to be made very quickly with memecoins — along with a ton of risk. Trading these tokens so quickly, tokens that are so widely volatile, is essentially a game. It’s a competition between those with insider knowledge, those without, the holders with the most assets, chart analysts who can buy or sell at the right time, those with proper exit strategies, those using bots and AI to immediately purchase new tokens upon launch, and those who can tell which memes are here to stay and which are fleeting. Memecoin trading is, at its core, about trading memetic culture. To me, this is the future of gaming and finance — an intersection that will be the bedrock of Web3, the future of the internet. Blockchain technology allows for instant transactions and trading of digital assets. This opens up the possibility for the auctioning and trading of in-game assets. I believe that Artificial intelligence will serve as the automation layer to facilitate the development of assets and process transactions. AI will make scouting for tradable items intelligent and efficient — essentially gamifying economics. Gaming will be the activity, AI will be the processing mechanism, and together they will bridge the gap to massive cryptocurrency adoption. In the future, assets and money will be tokenized, and blockchain will facilitate the movement of massive amounts of data, making NFTs more accessible. Gaming is key because people already collect digital assets in video games. If these in-game assets could be traded for currency or used to bid on other tangible and digital items, we’d see a more rapid flow of money throughout the industry and some incredible “gamer to investor” stories. What we’re seeing now are the foundational elements of a world similar to Ready Player One (2018), where the line between the digital and real world is blurred. The world is increasingly gamified. What will this mean for the future? How will people understand culture when trading culture is the future? This is not financial advice, but I urge you to research memecoins and cryptocurrency. The world is being digitized as we speak. I believe the players need to invest and the investors need to play. Is it crazy that we may be witnessing a collision of the serious and the unserious, the comedy meme and the business world? Could this explain the relaxed attire of business moguls due to the influence of tech industry CEOs? Will high fashion and sartorial works be that of memes? Will everything of timelessness become a meme? Is becoming meme the mummification of great ideas? Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Gamification and the future of culture. Memecoins are cryptocurrency tokens inspired by internet memes. By now, you’ve probably heard of $DOGE coin, the first-ever memecoin launched in 2013 as a joke about Bitcoin. Billy Markus, the co-creator of Doge, sold his holdings for a modest $10,000. Today, the market cap of DOGE is $52 billion. If Billy had held onto his DOGE coins, they’d be worth almost $20 million today. $DOGE Dare I say, memecoins are here to stay. There are probably hundreds of thousands of unique memecoins in existence today — some have gone to zero, others are dormant, some are still in flux, and a few are seeing steady growth. I have done freelance work in the crypto industry since 2018, but I never fully ventured into memecoins. I thought Bitcoin, a more established cryptocurrency, was volatile enough. When you’re just getting used to the concept of volatile markets and digital, invisible money, diving into something even less established, more volatile, and based on a meme seemed reckless. Why invest in a joke? At the time, it all seemed shady. It took me a few years to truly understand the appeal of memecoins. The surge in Bitcoin that followed Donald Trump’s win in the 2024 presidential election gave me perspective. BTC hit $67,000 on November 5th, 2024. After Trump, a self-proclaimed pro-Bitcoin president, was declared the winner, the price shot up over 39%, currently sitting at $94,000. This is an insane amount of growth — a new all-time high for Bitcoin. As the crypto surge unfolded, I noticed that memecoins were surging alongside Bitcoin. Google trends for crypto-related searches were going up, and people were claiming massive 300x returns on memecoin investments — truly life-changing money. The prices of these alternative coins are incredibly volatile. For example, take $PNUT , which is based on an internet-famous squirrel that was confiscated and euthanized by the New York government. Its price has shot up 1692% since November 4th, after Elon Musk tweeted about the late squirrel, saying, “America was saved by a squirrel and a meme coin!” Of course, the tweet boosted the price even further. $PNUT I decided to buy some Solana since many memecoins use the Solana blockchain due to its fast transaction speeds. Hundreds of coins are created every minute on the Solana ecosystem. You can easily and quickly send and receive Solana to buy many popular and rising memecoins. I traded, made some money, and lost some too. But I learned something that, perhaps, many already in the trenches of memecoin trading have long known: there is an insane amount of money to be made very quickly with memecoins — along with a ton of risk. Trading these tokens so quickly, tokens that are so widely volatile, is essentially a game. It’s a competition between those with insider knowledge, those without, the holders with the most assets, chart analysts who can buy or sell at the right time, those with proper exit strategies, those using bots and AI to immediately purchase new tokens upon launch, and those who can tell which memes are here to stay and which are fleeting. Memecoin trading is, at its core, about trading memetic culture. To me, this is the future of gaming and finance — an intersection that will be the bedrock of Web3, the future of the internet. Blockchain technology allows for instant transactions and trading of digital assets. This opens up the possibility for the auctioning and trading of in-game assets. I believe that Artificial intelligence will serve as the automation layer to facilitate the development of assets and process transactions. AI will make scouting for tradable items intelligent and efficient — essentially gamifying economics. Gaming will be the activity, AI will be the processing mechanism, and together they will bridge the gap to massive cryptocurrency adoption. In the future, assets and money will be tokenized, and blockchain will facilitate the movement of massive amounts of data, making NFTs more accessible. Gaming is key because people already collect digital assets in video games. If these in-game assets could be traded for currency or used to bid on other tangible and digital items, we’d see a more rapid flow of money throughout the industry and some incredible “gamer to investor” stories. What we’re seeing now are the foundational elements of a world similar to Ready Player One (2018), where the line between the digital and real world is blurred. The world is increasingly gamified. What will this mean for the future? How will people understand culture when trading culture is the future? Ready Player One This is not financial advice, but I urge you to research memecoins and cryptocurrency. The world is being digitized as we speak. I believe the players need to invest and the investors need to play. Is it crazy that we may be witnessing a collision of the serious and the unserious, the comedy meme and the business world? Could this explain the relaxed attire of business moguls due to the influence of tech industry CEOs? Will high fashion and sartorial works be that of memes? Will everything of timelessness become a meme? Is becoming meme the mummification of great ideas? Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR

Memecoins May Be Giving Birth to a Future Where The Lines Between Digital and Real Are Blurred

How to Build SaaS Multi-Tenant Applications: Multi-Tenant SaaS in 200 Lines of Code

Authors:
(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);
(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);
(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.2 Anchor Regression 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation B Experiments 4 Experiments We experimentally investigate and compare the performance of ADA. First, we use ADA in an in-distribution setting for a linear regression problem (Section 4.1), in which we show that even in this case, ADA provides improved performance in the low data regime. Second, in Section 4.2, we apply ADA and C-Mixup to the California and Boston Housing datasets as we increase the number of training samples. In the last two subsections, we replicate the in-distribution generalization (Section 4.3) and the out-of-distribution Robustness (Section 4.4) from the C-Mixup paper [49]. In [49] the authors further assess a task generalization experiment. However, the corresponding code was not publicly provided, and a comparison could not be easily made. 4.1 Linear synthetic data Using synthetic linear data, we investigate if ADA can improve model performance in an overparameterized setting compared to C-Mixup, vanilla augmentation, or classical expected risk minimization (ERM). Additionally, we analyze the sensitivity of our approach to the choice of γ and the number of augmentations. Data: The generated data follows a standard linear structure Models and Comparisons: We investigate and compare the impact of ADA using two different models with varying complexity: a linear Ridge regression and a multilayer perceptron (MLP) with one hidden layer and 10 units with ReLU activation. Using an MLP with more hidden layers shows similar results (see Appendix B.1 for details). For the Ridge regression model, we increase the dataset by a factor of 10 by sampling from the respective augmentation methods and subsequently compute the regression estimators. In contrast, for the MLP, we implement the augmentation methods on a minibatch level. Specifically, we incorporate vanilla augmentation by adding Gaussian noise to each batch, apply C-Mixup after sampling from the beta distribution in each batch, and finally, apply ADA after sampling from the defined gamma values in each batch. In summary, even in the simplest of cases, in which we should not expect gains from ADA (or C-Mixup), these data augmentation strategies provide gains in performance when the number of training examples is not sufficient to achieve the error floor. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Authors: Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.1 Data Augmentation 2.2 Anchor Regression 2.2 Anchor Regression 3 Anchor Data Augmentation 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation A Additional information for Anchor Data Augmentation B Experiments B Experiments 4 Experiments We experimentally investigate and compare the performance of ADA. First, we use ADA in an in-distribution setting for a linear regression problem (Section 4.1), in which we show that even in this case, ADA provides improved performance in the low data regime. Second, in Section 4.2, we apply ADA and C-Mixup to the California and Boston Housing datasets as we increase the number of training samples. In the last two subsections, we replicate the in-distribution generalization (Section 4.3) and the out-of-distribution Robustness (Section 4.4) from the C-Mixup paper [49]. In [49] the authors further assess a task generalization experiment. However, the corresponding code was not publicly provided, and a comparison could not be easily made. 4.1 Linear synthetic data Using synthetic linear data, we investigate if ADA can improve model performance in an overparameterized setting compared to C-Mixup, vanilla augmentation, or classical expected risk minimization (ERM). Additionally, we analyze the sensitivity of our approach to the choice of γ and the number of augmentations. Data : The generated data follows a standard linear structure Data Models and Comparisons: We investigate and compare the impact of ADA using two different models with varying complexity: a linear Ridge regression and a multilayer perceptron (MLP) with one hidden layer and 10 units with ReLU activation. Using an MLP with more hidden layers shows similar results (see Appendix B.1 for details). Models and Comparisons: For the Ridge regression model, we increase the dataset by a factor of 10 by sampling from the respective augmentation methods and subsequently compute the regression estimators. In contrast, for the MLP, we implement the augmentation methods on a minibatch level. Specifically, we incorporate vanilla augmentation by adding Gaussian noise to each batch, apply C-Mixup after sampling from the beta distribution in each batch, and finally, apply ADA after sampling from the defined gamma values in each batch. In summary, even in the simplest of cases, in which we should not expect gains from ADA (or C-Mixup), these data augmentation strategies provide gains in performance when the number of training examples is not sufficient to achieve the error floor. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Evaluating ADA: Experimental Results on Linear and Housing Datasets

Understanding requestAnimationFrame, Linear Interpolation, and CSS Transitions in Web Animations

Command Line Confessions: The Embarrassing Questions I Should Have Asked Long Ago

HackerNoon: What is your company in 2–5 words? Vladimir Danilla: Linearity makes a platform for graphic and motion design software. Why is now the time for your company to exist? Today, a lot of communication is images or video. We make design tools that anyone can use, even if you're not an expert or just not interested in buying expensive software packages. For us it feels like product design has a very great packaging now with Figma but in the marketing and animation world there's a huge gap that nobody does well yet. What do you love about your team, and why are you the ones to solve this problem? Our team is great at executing fast, and this is what we need to win the established players. It's not the big eating the small; it's the fast that eat the slow. If you weren’t building your startup, what would you be doing? Initially, I wasn't trying to build a startup. I was just solving my own issues with design software that I wasn’t satisfied with. Later on, that turned into Linearity, and I'm glad I'm not doing this alone anymore. If I didn’t have the company, I’d likely still be working on similar problems. At the moment, how do you measure success? What are your metrics? Being a user-centric startup, we’re measuring both our growth in terms of user numbers and assets created, as well as customer satisfaction. Our software is used to create millions of design assets every week. In a few sentences, what do you offer to whom? We're a comprehensive design platform, tailored for designers in marketing who work with graphics and motion design. The platform includes Linearity Curve, graphic design software, and Linearity Move, which lets businesses create their own high-quality animations in-house and without motion graphics experts. This boosts productivity, reduces costs, and makes marketing efforts more effective. What’s most exciting about your traction to date? Professionals at top companies like Disney, Marvel, BBC, and Apple use and love our software. Where do you think your growth will be next year? Linearity Curve is already used to create millions of assets every week, and having just launched Linearity Move, we hope it reaches the same level of usage by next year. Tell us about your first paying customer and revenue expectations over the next year. Linearity Curve was formerly known as Vectornator and was available free of charge. We’ve only recently created a freemium offer and a lot of our free user base has already converted. What’s your biggest threat? The major players in our field are companies like Adobe and Canva. However, we think that coping with the rise of AI will be key to success, and we are well-prepared to do just that. HackerNoon : What is your company in 2–5 words? HackerNoon Vladimir Danilla : Linearity makes a platform for graphic and motion design software. Vladimir Danilla Vladimir Danilla Why is now the time for your company to exist? Today, a lot of communication is images or video. We make design tools that anyone can use, even if you're not an expert or just not interested in buying expensive software packages. For us it feels like product design has a very great packaging now with Figma but in the marketing and animation world there's a huge gap that nobody does well yet. What do you love about your team, and why are you the ones to solve this problem? Our team is great at executing fast, and this is what we need to win the established players. It's not the big eating the small; it's the fast that eat the slow. If you weren’t building your startup, what would you be doing? Initially, I wasn't trying to build a startup. I was just solving my own issues with design software that I wasn’t satisfied with. Later on, that turned into Linearity , and I'm glad I'm not doing this alone anymore. If I didn’t have the company, I’d likely still be working on similar problems. Linearity At the moment, how do you measure success? What are your metrics? Being a user-centric startup, we’re measuring both our growth in terms of user numbers and assets created, as well as customer satisfaction. Our software is used to create millions of design assets every week. In a few sentences, what do you offer to whom? We're a comprehensive design platform, tailored for designers in marketing who work with graphics and motion design. The platform includes Linearity Curve, graphic design software, and Linearity Move, which lets businesses create their own high-quality animations in-house and without motion graphics experts. This boosts productivity, reduces costs, and makes marketing efforts more effective. What’s most exciting about your traction to date? Professionals at top companies like Disney, Marvel, BBC, and Apple use and love our software. Where do you think your growth will be next year? Linearity Curve is already used to create millions of assets every week, and having just launched Linearity Move, we hope it reaches the same level of usage by next year. Tell us about your first paying customer and revenue expectations over the next year. Linearity Curve was formerly known as Vectornator and was available free of charge. We’ve only recently created a freemium offer and a lot of our free user base has already converted. What’s your biggest threat? The major players in our field are companies like Adobe and Canva. However, we think that coping with the rise of AI will be key to success, and we are well-prepared to do just that.

"It's not the big eating the small; it's the fast that eat the slow." says CEO/Founder of Linearity

"Janiw jach'a jaqinakax jisk'a manq'apkiti; sasaw CEO/Fundador de Linealidad ukax saraki

"No es el grande el que se come al pequeño, sino el rápido el que se come al lento", afirma el CEO y fundador de Linearity

"ऐसा नहीं है कि बड़े लोग छोटे लोगों को खा जाते हैं; बल्कि तेज़ लोग धीमे लोगों को खा जाते हैं।" लीनियरिटी के सीईओ/संस्थापक कहते हैं

「大きなものが小さなものを食べるのではなく、速いものが遅いものを食べるのです」とリニアリティのCEO兼創設者は言う。

"큰 것이 작은 것을 먹는 것이 아니라 빠른 것이 느린 것을 먹는다"고 Linearity의 CEO/창립자가 말했습니다.

"ມັນບໍ່ແມ່ນໃຫຍ່ກິນຂະຫນາດນ້ອຍ; ມັນແມ່ນໄວທີ່ກິນຊ້າ." CEO/ຜູ້ກໍ່ຕັ້ງຂອງ Linearity ກ່າວ

"Tsy ny lehibe mihinana ny kely, fa ny mifady hanina no mihinana ny miadana." hoy ny tale jeneralin'ny orinasa / mpanorina ny Linearity

"Het zijn niet de groten die de kleinen opeten; het zijn de snellen die de langzamen opeten", zegt CEO/oprichter van Linearity

«Это не большие едят маленьких, это быстрые едят медленных», — говорит генеральный директор/основатель Linearity.

"එය ලොකු අය කුඩා දේ අනුභව කිරීම නොවේ; එය මන්දගාමීව අනුභව කරන වේගවත් ය." Linearity හි ප්‍රධාන විධායක නිලධාරී/ නිර්මාතෘ පවසයි

"Nie veľkí jedia malých, ale rýchli jedia pomaly." hovorí generálny riaditeľ/zakladateľ spoločnosti Linearity

"یہ بڑا نہیں ہے جو چھوٹا کھاتا ہے؛ یہ تیز ہے جو آہستہ کھاتا ہے۔" سی ای او / لائنیرٹی کے بانی کہتے ہیں۔

Line Breaks To create a line break or new line ( ), end a line with two or more spaces, and then type return. Markdown

HTML

Rendered Output



This is the first line.  And this is the second line.

This is the first line. And this is the second line.

This is the first line.And this is the second line. Line Break Best Practices You can use two or more spaces (commonly referred to as “trailing whitespace”) for line breaks in nearly every Markdown application, but it’s controversial. It’s hard to see trailing whitespace in an editor, and many people accidentally or intentionally put two spaces after every sentence. For this reason, you may want to use something other than trailing whitespace for line breaks. If your Markdown application supports HTML, you can use the   HTML tag. For compatibility, use trailing white space or the   HTML tag at the end of the line. There are two other options I don’t recommend using. CommonMark and a few other lightweight markup languages let you type a backslash (\) at the end of the line, but not all Markdown applications support this, so it isn’t a great option from a compatibility perspective. And at least a couple lightweight markup languages don’t require anything at the end of the line — just type return and they’ll create a line break. ✅  Do this

❌  Don't do this



First line with two spaces after.  And the next line.First line with the HTML tag after. And the next line.

First line with a backslash after.\And the next line.First line with nothing after.And the next line. This guide is a Matt Cone project available on Markdown Guide under the CC BY-SA 4.0 license. Line Breaks To create a line break or new line ( ), end a line with two or more spaces, and then type return. Markdown

HTML

Rendered Output



This is the first line.  And this is the second line.

This is the first line. And this is the second line.

This is the first line.And this is the second line. Markdown

HTML

Rendered Output This is the first line.  And this is the second line.

This is the first line. And this is the second line.

This is the first line.And this is the second line. Markdown HTML Rendered Output Markdown Markdown HTML HTML Rendered Output Rendered Output This is the first line.  And this is the second line. This is the first line. And this is the second line. This is the first line.And this is the second line. This is the first line.  And this is the second line. This is the first line. And this is the second line. This is the first line. And this is the second line. This is the first line. And this is the second line. This is the first line. And this is the second line. This is the first line. And this is the second line. This is the first line.And this is the second line. This is the first line. And this is the second line. Line Break Best Practices You can use two or more spaces (commonly referred to as “trailing whitespace”) for line breaks in nearly every Markdown application, but it’s controversial. It’s hard to see trailing whitespace in an editor, and many people accidentally or intentionally put two spaces after every sentence. For this reason, you may want to use something other than trailing whitespace for line breaks. If your Markdown application supports HTML , you can use the HTML tag. supports HTML For compatibility, use trailing white space or the HTML tag at the end of the line. There are two other options I don’t recommend using. CommonMark and a few other lightweight markup languages let you type a backslash ( \ ) at the end of the line, but not all Markdown applications support this, so it isn’t a great option from a compatibility perspective. And at least a couple lightweight markup languages don’t require anything at the end of the line — just type return and they’ll create a line break. \ ✅  Do this

❌  Don't do this



First line with two spaces after.  And the next line.First line with the HTML tag after. And the next line.

First line with a backslash after.\And the next line.First line with nothing after.And the next line. ✅  Do this

❌  Don't do this First line with two spaces after.  And the next line.First line with the HTML tag after. And the next line.

First line with a backslash after.\And the next line.First line with nothing after.And the next line. ✅  Do this ❌  Don't do this ✅  Do this ✅  Do this ❌  Don't do this ❌  Don't do this First line with two spaces after.  And the next line.First line with the HTML tag after. And the next line. First line with a backslash after.\And the next line.First line with nothing after.And the next line. First line with two spaces after.  And the next line.First line with the HTML tag after. And the next line. First line with two spaces after. And the next line. First line with the HTML tag after. And the next line. First line with two spaces after. And the next line. First line with the HTML tag after. And the next line. First line with a backslash after.\And the next line.First line with nothing after.And the next line. First line with a backslash after.\ And the next line. First line with nothing after. And the next line. First line with a backslash after.\ And the next line. First line with nothing after. And the next line. This guide is a Matt Cone project available on Markdown Guide under the CC BY-SA 4.0 license. This guide is a Matt Cone project available on Markdown Guide under the CC BY-SA 4.0 license. Matt Cone Markdown Guide

The Dead Simple Markdown Guide to Line Breaks

Authors:
(1) Li Siyao, S-Lab, Nanyang Technological University;
(2) Tianpei Gu, Lexica and Work completed at UCLA;
(3) Weiye Xiao, Southeast University;
(4) Henghui Ding, S-Lab, Nanyang Technological University;
(5) Ziwei Liu, S-Lab, Nanyang Technological University;
(6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Table of Links Abstract and 1. Introduction Related Work
Mixamo Line Art Dataset
Our Approach
Experiment
Conclusion and References 6. Conclusion In this study, we address the practical problem of cartoon line inbetweening and propose a novel approach that treats line arts as geometrized vector graphs. Unlike previous frame interpolation tasks on raster images, our approach formulates the inbetweening task as a graph fusion problem with vertex repositioning. We present a deep learning-based framework called AnimeInbet, which shows significant gains over existing methods in terms of both quantitative and qualitative evaluation. To facilitate training and evaluation on cartoon line inbetweening, we also provide a large-scale geometrized line art dataset, MixamoLine240. Our proposed framework and dataset facilitate a wide range of applications, such as anime production and multimedia design, and have significant practical implications. Acknowledgement. This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-PhD/2021-01- 031[T]). It is also supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). This study is partially supported by NTU NAP, MOE AcRF Tier 1 (2021-T1-001-088). References [1] Mixamo. https://www.mixamo.com/. 3 [2] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373–1396, 2003. 5 [3] Leonardo Carvalho, Ricardo Marroquim, and Emilio Vital Brazil. Dilight: Digital light table–inbetweening for 2d animations using guidelines. Computers & Graphics, 2017. 3 [4] Evan Casey, V´ıctor Perez, and Zhuoru Li. The animation ´ transformer: Visual correspondence via segment matching. In ICCV, 2021. 1, 3 [5] Shuhong Chen and Matthias Zwicker. Improving the perceptual quality of 2d animation interpolation. In ECCV, 2022. 1, 2, 6, 7 [6] Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time intermediate flow estimation for video frame interpolation. In ECCV, 2022. 1, 2, 6, 7 [7] Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, and Jan Kautz. Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. In CVPR, 2018. 2 [8] Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, and Jaegul Choo. Animeceleb: Largescale animation celebheads dataset for head reenactment. In ECCV, 2022. 3 [9] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2014. 7 [10] Johannes Kopf and Dani Lischinski. Digital reconstruction of halftoned color comics. ACM TOG, 31(6), 2012. 1, 3 [11] Xiaoyu Li, Bo Zhang, Jing Liao, and Pedro V. Sander. Deep sketch-guided cartoon video inbetweening. TVCG, 2020. 2 [12] Songtao Liu, Jin Huang, and Hao Zhang. End-to-end line drawing vectorization. In AAAI, 2022. 3, 4 [13] Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala. Video frame synthesis using deep voxel flow. In CVPR, 2017. 2 [14] Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, and Jiaya Jia. Video frame interpolation with transformer. In CVPR, 2022. 1, 2, 6, 7 [15] Haoran Mo, Edgar Simo-Serra, Chengying Gao, Changqing Zou, and Ruomei Wang. General virtual sketching framework for vector line art. In SIGGRAPH, 2021. 3, 4, 7, 9 [16] Rei Narita, Keigo Hirakawa, and Kiyoharu Aizawa. Optical flow based line drawing frame interpolation using distance transform to support inbetweenings. In ICIP, 2019. 7 [17] Simon Niklaus and Feng Liu. Context-aware synthesis for video frame interpolation. In CVPR, 2018. 2 [18] Simon Niklaus and Feng Liu. Softmax splatting for video frame interpolation. In CVPR, 2020. 2 [19] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive convolution. In CVPR, 2017. 2 [20] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive separable convolution. In ICCV, 2017. 2 [21] Junheum Park, Keunsoo Ko, Chul Lee, and Chang-Su Kim. Bmbc: Bilateral motion estimation with bilateral cost volume for video interpolation. In ECCV, 2020. 2 [22] Yingge Qu, Tien-Tsin Wong, and Pheng-Ann Heng. Manga colorization. ACM TOG, 25(3), 2006. 1, 3 [23] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In ECCV, 2022. 1, 2, 6, 7 [24] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In CVPR, 2020. 5 [25] Maria Shugrina, Ziheng Liang, Amlan Kar, Jiaman Li, Angad Singh, Karan Singh, and Sanja Fidler. Creative flow+ dataset. In CVPR, 2019. 3 [26] Hyeonjun Sim, Jihyong Oh, and Munchurl Kim. Xvfi: extreme video frame interpolation. In ICCV, 2021. 2 [27] Edgar Simo-Serra, Satoshi Iizuka, and Hiroshi Ishikawa. Mastering sketching: Adversarial augmentation for structured prediction. ACM TOG, 37(1), 2018. 2 [28] Edgar Simo-Serra, Satoshi Iizuka, Kazuma Sasaki, and Hiroshi Ishikawa. Learning to simplify: Fully convolutional networks for rough sketch cleanup. ACM TOG, 35(4), 2016. 2 [29] Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, and Chen Change Loy. Animerun: 2d animation visual correspondence from open source 3d movies. In NeurIPS, 2022. 3 [30] Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, and Ziwei Liu. Deep animation video interpolation in the wild. In CVPR, 2021. 2 [31] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr: Detector-free local feature matching with transformers. In CVPR, 2021. 5 [32] D. Sykora, J. Buri ` anek, and J. ´ Zˇ ara. Unsupervised coloriza- ´ tion of black-and-white cartoons. In Int. Symp. NPAR, 2004. 1, 3 [33] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In CVPR, 2022. 5 [34] Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, and MingHsuan Yang. Quadratic video interpolation. In NeurIPS, 2019. 2 [35] Wenwu Yang. Context-aware computer aided inbetweening. IEEE TVCG, 24(2):1049–1062, 2017. 3 [36] Chih-Yuan Yao, Shih-Hsuan Hung, Guo-Wei Li, I-Yu Chen, Reza Adhitya, and Yu-Chi Lai. Manga vectorization and manipulation with procedural simple screentone. IEEE TVCG, 23(2), 2016. 3, 4 [37] Lvmin Zhang and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023. 3 [38] Lvmin Zhang, Jinyue Jiang, Yi Ji, and Chunping Liu. Smartshadow: Artistic shadow drawing tool for line drawings. In ICCV, 2021. 1, 3 [39] Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, and Chunping Liu. Two-stage sketch colorization. In SIGGRAPH, 2018. 1, 3 [40] Song-Hai Zhang, Tao Chen, Yi-Fei Zhang, Shi-Min Hu, and Ralph R. Martin. Vectorizing cartoon animations. IEEE TVCG, 15(4), 2009. 3, 4 This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Li Siyao, S-Lab, Nanyang Technological University; (2) Tianpei Gu, Lexica and Work completed at UCLA; (3) Weiye Xiao, Southeast University; (4) Henghui Ding, S-Lab, Nanyang Technological University; (5) Ziwei Liu, S-Lab, Nanyang Technological University; (6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Authors: Authors: (1) Li Siyao, S-Lab, Nanyang Technological University; (2) Tianpei Gu, Lexica and Work completed at UCLA; (3) Weiye Xiao, Southeast University; (4) Henghui Ding, S-Lab, Nanyang Technological University; (5) Ziwei Liu, S-Lab, Nanyang Technological University; (6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work Mixamo Line Art Dataset Our Approach Experiment Conclusion and References Related Work Related Work Mixamo Line Art Dataset Mixamo Line Art Dataset Our Approach Our Approach Experiment Experiment Conclusion and References Conclusion and References 6. Conclusion In this study, we address the practical problem of cartoon line inbetweening and propose a novel approach that treats line arts as geometrized vector graphs. Unlike previous frame interpolation tasks on raster images, our approach formulates the inbetweening task as a graph fusion problem with vertex repositioning. We present a deep learning-based framework called AnimeInbet , which shows significant gains over existing methods in terms of both quantitative and qualitative evaluation. To facilitate training and evaluation on cartoon line inbetweening, we also provide a large-scale geometrized line art dataset, MixamoLine240 . Our proposed framework and dataset facilitate a wide range of applications, such as anime production and multimedia design, and have significant practical implications. AnimeInbet MixamoLine240 Acknowledgement . This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-PhD/2021-01- 031[T]). It is also supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). This study is partially supported by NTU NAP, MOE AcRF Tier 1 (2021-T1-001-088). Acknowledgement References [1] Mixamo. https://www.mixamo.com/ . 3 https://www.mixamo.com/ [2] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373–1396, 2003. 5 [3] Leonardo Carvalho, Ricardo Marroquim, and Emilio Vital Brazil. Dilight: Digital light table–inbetweening for 2d animations using guidelines. Computers & Graphics, 2017. 3 [4] Evan Casey, V´ıctor Perez, and Zhuoru Li. The animation ´ transformer: Visual correspondence via segment matching. In ICCV, 2021. 1, 3 [5] Shuhong Chen and Matthias Zwicker. Improving the perceptual quality of 2d animation interpolation. In ECCV, 2022. 1, 2, 6, 7 [6] Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time intermediate flow estimation for video frame interpolation. In ECCV, 2022. 1, 2, 6, 7 [7] Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, and Jan Kautz. Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. In CVPR, 2018. 2 [8] Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, and Jaegul Choo. Animeceleb: Largescale animation celebheads dataset for head reenactment. In ECCV, 2022. 3 [9] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2014. 7 [10] Johannes Kopf and Dani Lischinski. Digital reconstruction of halftoned color comics. ACM TOG, 31(6), 2012. 1, 3 [11] Xiaoyu Li, Bo Zhang, Jing Liao, and Pedro V. Sander. Deep sketch-guided cartoon video inbetweening. TVCG, 2020. 2 [12] Songtao Liu, Jin Huang, and Hao Zhang. End-to-end line drawing vectorization. In AAAI, 2022. 3, 4 [13] Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala. Video frame synthesis using deep voxel flow. In CVPR, 2017. 2 [14] Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, and Jiaya Jia. Video frame interpolation with transformer. In CVPR, 2022. 1, 2, 6, 7 [15] Haoran Mo, Edgar Simo-Serra, Chengying Gao, Changqing Zou, and Ruomei Wang. General virtual sketching framework for vector line art. In SIGGRAPH, 2021. 3, 4, 7, 9 [16] Rei Narita, Keigo Hirakawa, and Kiyoharu Aizawa. Optical flow based line drawing frame interpolation using distance transform to support inbetweenings. In ICIP, 2019. 7 [17] Simon Niklaus and Feng Liu. Context-aware synthesis for video frame interpolation. In CVPR, 2018. 2 [18] Simon Niklaus and Feng Liu. Softmax splatting for video frame interpolation. In CVPR, 2020. 2 [19] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive convolution. In CVPR, 2017. 2 [20] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive separable convolution. In ICCV, 2017. 2 [21] Junheum Park, Keunsoo Ko, Chul Lee, and Chang-Su Kim. Bmbc: Bilateral motion estimation with bilateral cost volume for video interpolation. In ECCV, 2020. 2 [22] Yingge Qu, Tien-Tsin Wong, and Pheng-Ann Heng. Manga colorization. ACM TOG, 25(3), 2006. 1, 3 [23] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In ECCV, 2022. 1, 2, 6, 7 [24] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In CVPR, 2020. 5 [25] Maria Shugrina, Ziheng Liang, Amlan Kar, Jiaman Li, Angad Singh, Karan Singh, and Sanja Fidler. Creative flow+ dataset. In CVPR, 2019. 3 [26] Hyeonjun Sim, Jihyong Oh, and Munchurl Kim. Xvfi: extreme video frame interpolation. In ICCV, 2021. 2 [27] Edgar Simo-Serra, Satoshi Iizuka, and Hiroshi Ishikawa. Mastering sketching: Adversarial augmentation for structured prediction. ACM TOG, 37(1), 2018. 2 [28] Edgar Simo-Serra, Satoshi Iizuka, Kazuma Sasaki, and Hiroshi Ishikawa. Learning to simplify: Fully convolutional networks for rough sketch cleanup. ACM TOG, 35(4), 2016. 2 [29] Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, and Chen Change Loy. Animerun: 2d animation visual correspondence from open source 3d movies. In NeurIPS, 2022. 3 [30] Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, and Ziwei Liu. Deep animation video interpolation in the wild. In CVPR, 2021. 2 [31] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr: Detector-free local feature matching with transformers. In CVPR, 2021. 5 [32] D. Sykora, J. Buri ` anek, and J. ´ Zˇ ara. Unsupervised coloriza- ´ tion of black-and-white cartoons. In Int. Symp. NPAR, 2004. 1, 3 [33] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In CVPR, 2022. 5 [34] Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, and MingHsuan Yang. Quadratic video interpolation. In NeurIPS, 2019. 2 [35] Wenwu Yang. Context-aware computer aided inbetweening. IEEE TVCG, 24(2):1049–1062, 2017. 3 [36] Chih-Yuan Yao, Shih-Hsuan Hung, Guo-Wei Li, I-Yu Chen, Reza Adhitya, and Yu-Chi Lai. Manga vectorization and manipulation with procedural simple screentone. IEEE TVCG, 23(2), 2016. 3, 4 [37] Lvmin Zhang and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023. 3 [38] Lvmin Zhang, Jinyue Jiang, Yi Ji, and Chunping Liu. Smartshadow: Artistic shadow drawing tool for line drawings. In ICCV, 2021. 1, 3 [39] Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, and Chunping Liu. Two-stage sketch colorization. In SIGGRAPH, 2018. 1, 3 [40] Song-Hai Zhang, Tao Chen, Yi-Fei Zhang, Shi-Min Hu, and Ralph R. Martin. Vectorizing cartoon animations. IEEE TVCG, 15(4), 2009. 3, 4 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

Deep Geometrized Cartoon Line Inbetweening: Conclusion and References

Authors:
(1) Li Siyao, S-Lab, Nanyang Technological University;
(2) Tianpei Gu, Lexica and Work completed at UCLA;
(3) Weiye Xiao, Southeast University;
(4) Henghui Ding, S-Lab, Nanyang Technological University;
(5) Ziwei Liu, S-Lab, Nanyang Technological University;
(6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Table of Links Abstract and 1. Introduction Related Work
Mixamo Line Art Dataset
Our Approach
Experiment
Conclusion and References 5. Experiment Evaluation Metric. Following [16, 5], we adopt the chamfer distance (CD) as the evaluation metric, which has been initially introduced to measure the similarity between two point clouds. Formally, CD is computed as: 5.1. Comparison to Existing Methods As shown in Table 2, our AnimeInbet favorably outperforms all compared methods on both the validation set and the test set of MixamoLine240. On the validation set, our approach achieves an average CD value of 11.53, representing a significant improvement over the best-performing compared method, FILM, with over 30% enhancement. Upon closer inspection, the advantage of AnimeInbet becomes more pronounced as the frame gap increases (0.98, 5.72 and 9.47 for gaps of 1, 5, and 9, respectively), indicating that our method is more robust in handling larger motions. On the test set, our method maintains its lead over the other compared methods, with improvements of 0.70 (20%), 5.25 (29%), and 10.30 (31%) from the best-performing compared method FILM for the frame gaps of 1, 5, and 9, respectively. Given that both the characters and actions in the test set are new, our method’s superiority on the test set provides more convincing evidence of its advantages over the existing frame interpolation methods. To illustrate the advantages of our method, we present several inbetweening results in Figure 8. We arranged these examples in increasing levels of difficulty from top to bottom. When the motion is simple, compared methods can interpolate a relatively complete shape of the main body of the drawing. However, they tend to produce strong blurring (RIFE) or disappearance (VFIformer, EISAI, and FILM) of noticeable moving compositions (indicated by red arrows). In contrast, our method maintains a concise line structure in these key areas. When the input frames involve the whole body’s movement within large magnitudes, the intermediate frames predicted by the compared methods become indistinguishable and patchy, rendering the results invalid for further use. However, our AnimeInbet method can still preserve the general shape in the correct positions, even with a partial loss of details, which can be easily fixed with minor manual effort. User Study. To further evaluate the visual performance of our methods, we conduct a user study among 36 participants. For each participant, we randomly show 60 pairs, each composed of a result of AnimeInbet and that of a compared method, and ask the participant to select the better. To allow participants to take temporal consistency into the decision, we display these results in GIF formats formed by triplets of input frames and the inbetweened one. The winning rates of our method are shown in Figure 9, where AnimeInbet wins over 92% versus the compared methods. Notably, for “gap = 5” and “gap = 9” slots, the winning rates of our methods are close to 100% with smaller deviations than “gap = 1”, suggesting the advantages of our method on cases within large motions. 5.2. Ablation Study Embedding Features. To investigate the effectiveness of the three types of embeddings mentioned in Section 4.1, we trained several variants by removing the corresponding modules. As shown in Table 3, for each variant, we list the matching accuracy for all vertices (“Acc.”), the accuracy for non-occluded vertices (“Valid Acc.”) and the final CD values of inbetweening on the validation set (gap = 5). If removing the positional embedding EP , the “Valid Acc.” and the CD value drop 15.83% and 0.74, respectively; while the lacking of topological embedding ET lowers “Valid Acc.” by 5.66% and worsens CD by 0.43, which reveals the importance of these two components. Repositioning Propagation and Visibility Mask. We demonstrate the contribution of repositioning propagation (prepos. prop.) and visibility mask (vis. mask) both quantitatively and qualitatively. As shown in Table 4, without repositioning propagation, the CD value will be sharply worsened by 12.50 (112%), while the lacking of visibility mask will also make a drop of 1.69 (15%). An example is shown in Figure 10, where “w/o. repos. prop.” appears within many messy lines due to undefined positions for those unmatched vertices, while “w/o. vis. mask” shows some redundant segments (red box) after repositioning; the complete AnimeInbet can resolve these issues and produce a clean yet complete result. Geometrizor. As shown in Table 2, the quantitative metrics of AnimeInbet-VS are generally worse by around 0.6 compared to AnimeInbet. This is because VirtualSketcher [15] does not vectorize the line arts as precisely as the ground truth labels (average vertex number 587 vs 1,351). As shown in Figure 10, the curves in “AnimeInbet-VS” become sharper and lose some details, which decreases the quality of the inbetweened frame. Using a more accurate geometrizer would lead to higher quality inbetweening results for raster image inputs. Data Influence. As mentioned in Section 3, we created a validation set composed of 20 sequences of unseen characters but seen actions, 20 of unseen actions but seen characters and 4 of unseen both to explore the influence on data. Our experiment finds that whether the characters or the actions are seen does not fundamentally influence the inbetweening quality, while the motion magnitude is the key factor. As shown in Table 5, the CD value of unseen characters is 14.70, which is over 47% worse than that of unseen both due to larger vertex shifts (44.59 vs 29.62), while the difference between the CD values of unseen actions and unseen both is around 10% under similar occlusion rates and shifts. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Li Siyao, S-Lab, Nanyang Technological University; (2) Tianpei Gu, Lexica and Work completed at UCLA; (3) Weiye Xiao, Southeast University; (4) Henghui Ding, S-Lab, Nanyang Technological University; (5) Ziwei Liu, S-Lab, Nanyang Technological University; (6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Authors: Authors: (1) Li Siyao, S-Lab, Nanyang Technological University; (2) Tianpei Gu, Lexica and Work completed at UCLA; (3) Weiye Xiao, Southeast University; (4) Henghui Ding, S-Lab, Nanyang Technological University; (5) Ziwei Liu, S-Lab, Nanyang Technological University; (6) Chen Change Loy, S-Lab, Nanyang Technological University and a Corresponding Author. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work Mixamo Line Art Dataset Our Approach Experiment Conclusion and References Related Work Related Work Mixamo Line Art Dataset Mixamo Line Art Dataset Our Approach Our Approach Experiment Experiment Conclusion and References Conclusion and References 5. Experiment Evaluation Metric. Following [16, 5], we adopt the chamfer distance (CD) as the evaluation metric, which has been initially introduced to measure the similarity between two point clouds. Formally, CD is computed as: Evaluation Metric. 5.1. Comparison to Existing Methods As shown in Table 2, our AnimeInbet favorably outperforms all compared methods on both the validation set and the test set of MixamoLine240. On the validation set, our approach achieves an average CD value of 11.53, representing a significant improvement over the best-performing compared method, FILM, with over 30% enhancement. Upon closer inspection, the advantage of AnimeInbet becomes more pronounced as the frame gap increases (0.98, 5.72 and 9.47 for gaps of 1, 5, and 9, respectively), indicating that our method is more robust in handling larger motions. On the test set, our method maintains its lead over the other compared methods, with improvements of 0.70 (20%), 5.25 (29%), and 10.30 (31%) from the best-performing compared method FILM for the frame gaps of 1, 5, and 9, respectively. Given that both the characters and actions in the test set are new, our method’s superiority on the test set provides more convincing evidence of its advantages over the existing frame interpolation methods. AnimeInbet To illustrate the advantages of our method, we present several inbetweening results in Figure 8. We arranged these examples in increasing levels of difficulty from top to bottom. When the motion is simple, compared methods can interpolate a relatively complete shape of the main body of the drawing. However, they tend to produce strong blurring (RIFE) or disappearance (VFIformer, EISAI, and FILM) of noticeable moving compositions (indicated by red arrows). In contrast, our method maintains a concise line structure in these key areas. When the input frames involve the whole body’s movement within large magnitudes, the intermediate frames predicted by the compared methods become indistinguishable and patchy, rendering the results invalid for further use. However, our AnimeInbet method can still preserve the general shape in the correct positions, even with a partial loss of details, which can be easily fixed with minor manual effort. AnimeInbet User Study. To further evaluate the visual performance of our methods, we conduct a user study among 36 participants. For each participant, we randomly show 60 pairs, each composed of a result of AnimeInbet and that of a compared method, and ask the participant to select the better. To allow participants to take temporal consistency into the decision, we display these results in GIF formats formed by triplets of input frames and the inbetweened one. The winning rates of our method are shown in Figure 9, where AnimeInbet wins over 92% versus the compared methods. Notably, for “gap = 5” and “gap = 9” slots, the winning rates of our methods are close to 100% with smaller deviations than “gap = 1”, suggesting the advantages of our method on cases within large motions. User Study. 5.2. Ablation Study Embedding Features. To investigate the effectiveness of the three types of embeddings mentioned in Section 4.1, we trained several variants by removing the corresponding modules. As shown in Table 3, for each variant, we list the matching accuracy for all vertices (“Acc.”), the accuracy for non-occluded vertices (“Valid Acc.”) and the final CD values of inbetweening on the validation set (gap = 5). If removing the positional embedding EP , the “Valid Acc.” and the CD value drop 15.83% and 0.74, respectively; while the lacking of topological embedding ET lowers “Valid Acc.” by 5.66% and worsens CD by 0.43, which reveals the importance of these two components. Repositioning Propagation and Visibility Mask. We demonstrate the contribution of repositioning propagation (prepos. prop.) and visibility mask (vis. mask) both quantitatively and qualitatively. As shown in Table 4, without repositioning propagation, the CD value will be sharply worsened by 12.50 (112%), while the lacking of visibility mask will also make a drop of 1.69 (15%). An example is shown in Figure 10, where “w/o. repos. prop.” appears within many messy lines due to undefined positions for those unmatched vertices, while “w/o. vis. mask” shows some redundant segments (red box) after repositioning; the complete AnimeInbet can resolve these issues and produce a clean yet complete result. Repositioning Propagation and Visibility Mask. Geometrizor . As shown in Table 2, the quantitative metrics of AnimeInbet-VS are generally worse by around 0.6 compared to AnimeInbet . This is because VirtualSketcher [15] does not vectorize the line arts as precisely as the ground truth labels (average vertex number 587 vs 1,351). As shown in Figure 10, the curves in “ AnimeInbet-VS ” become sharper and lose some details, which decreases the quality of the inbetweened frame. Using a more accurate geometrizer would lead to higher quality inbetweening results for raster image inputs. Geometrizor AnimeInbet-VS AnimeInbet VirtualSketcher AnimeInbet-VS Data Influence. As mentioned in Section 3, we created a validation set composed of 20 sequences of unseen characters but seen actions, 20 of unseen actions but seen characters and 4 of unseen both to explore the influence on data. Our experiment finds that whether the characters or the actions are seen does not fundamentally influence the inbetweening quality, while the motion magnitude is the key factor. As shown in Table 5, the CD value of unseen characters is 14.70, which is over 47% worse than that of unseen both due to larger vertex shifts (44.59 vs 29.62), while the difference between the CD values of unseen actions and unseen both is around 10% under similar occlusion rates and shifts. Data Influence. 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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