This AI Performs Seamless Video Manipulation Without Deep Learning or Datasets

Have you ever wanted to edit a video to remove or add someone, change the background, make it last a bit longer, or change the resolution to fit a specific aspect ratio without compressing or stretching it? For those of you who already ran advertisement campaigns, you certainly wanted to have variations of your videos for AB testing and see what works best.

Well, this new research by Niv Haim et al. can help you do all of the about in a single video and in HD!

Indeed, using a simple video, you can perform any tasks I just mentioned in seconds or a few minutes for high-quality videos. You can basically use it for any video manipulation or video generation application you have in mind. It even outperforms GANs in all ways and doesn’t use any deep learning fancy research nor requires a huge and impractical dataset!

And the best thing is that this technique is scalable to high-resolution videos...

►Read the full article: https://www.louisbouchard.ai/vgpnn-ge...
►Paper covered: Haim, N., Feinstein, B., Granot, N., Shocher, A., Bagon, S., Dekel, T., & Irani, M. (2021). Diverse Generation from a Single Video Made Possible. ArXiv, abs/2109.08591.
►The technique that was adapted from images to videos: Niv Granot, Ben Feinstein, Assaf Shocher, Shai Bagon, and Michal Irani. Drop the gan: In defense of patches nearest neighbors as single image generative models. arXiv preprint arXiv:2103.15545, 2021.
►Code (available soon): https://nivha.github.io/vgpnn/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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have you ever wanted to edit a video

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remove or add someone change the

00:04

background make it last a bit longer or

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change the resolution to fit a specific

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aspect ratio without compressing or

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stretching it for those of you who

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already ran advertisement campaigns you

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certainly wanted to have variations of

00:16

your videos for a b testing and see what

00:19

works best well this new research by niv

00:22

haim ital can help you do all of these

00:24

out of a single video and in high

00:27

definition indeed using a simple video

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you can perform any tasks i just

00:32

mentioned in seconds or in a few minutes

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for high quality videos you can

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basically use it for any video

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manipulation or video generation

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application you have in mind it even

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outperforms guns in any ways and doesn't

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use any deep learning fancy research nor

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requires a huge and impractical data set

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and the best thing is that this

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technique is scalable to high resolution

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videos it is not only for research

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purposes with 256 by 256 pixel videos oh

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and of course you can use it with images

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let's see how it works the model is

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called video based generative patch

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nearest neighbors vgpnn instead of using

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complex algorithms and models like gans

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or transformers the researchers that

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developed vgpn opt for a much simpler

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approach but revisited the nearest

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neighbor algorithm first they downscale

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the image in a pyramid way where each

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level is a flower resolution than the

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one above then they add random noise to

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the coarsest level to generate a

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different image similar to what guns do

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in the compressed space after encoding

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the image note that here i will say

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image for simplicity but in this case

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since it's applied to videos the process

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is made on three frames simultaneously

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adding a time dimension but the

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explanation stays the same with an extra

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step at the end the image at the

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coarsest scale with noise added is

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divided into multiple small square

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patches all patches in the image with

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noise added are replaced with the most

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similar patch from the initial scaled

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down image without noise this most

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similar patch is measured with the

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nearest neighbor algorithm as we will

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see most of these patches will stay the

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same but depending on the added noise

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some patches will change just enough to

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make them look more similar to another

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patch in the initial image this is the

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vpn output you see here these changes

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are just enough to generate a new

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version of the image then this first

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output is upscaled and used to compare

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with the input image of the next scale

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to act as a noisy version of it and the

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same steps are repeated in this next

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iteration we split these images into

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small patches and replace the previously

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generated ones with the most similar

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ones at the current step let's get into

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this vpn module we just covered as you

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can see here the only difference from

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the initial step with noise added is

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that we compare the upscale generated

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image here denoted as q with an upscaled

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version of the previous image just so it

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has the same level of details denoted as

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k basically using the level below as

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comparisons we compare q and k and then

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select corresponding patches in the

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image from this current level v to

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generate the new image for this step

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which will be used for the next

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iteration as you see here with the small

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arrows k is just an upscale version of

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the image we created downscaling v in

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the initial step of this algorithm where

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we created the pyramidal scaling

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versions of our image this is done to

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compare the same level of sharpness in

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both images as the upscale generated

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image from the previous layer q will be

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much more blurry than the image at the

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current step v and it will be very hard

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to find similar patches this is repeated

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until we get back to the top of the

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pyramid with high resolution results

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then all these generated patches are

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folded into a video and voila you can

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repeat this with different noises or

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modifications to generate any variations

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you want on your videos let's do a quick

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recap the image is downscaled at

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multiple scales noise is added to the

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corsa scale image which is divided into

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small square patches each noisy patch is

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then replaced with the most similar

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patches from the same compressed image

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without noise causing few random changes

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in the image while keeping realism both

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the newly generated image and image

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without noise of this step are upscaled

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and compared to find the most similar

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patches with the nearest neighbor again

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these most similar patches are then

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chosen from the image at the current

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resolution to generate a new image for

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the step again and we repeat this

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upscaling and comparing steps until we

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get back to the top of the pyramid with

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high resolution results of course the

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results are not perfect you can still

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see some artifacts like people appearing

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and disappearing at weird places or

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simply copy-pasting someone in some

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cases making it very obvious if you

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focus on it still it's only the first

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paper attacking video manipulations with

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the nearest neighbor algorithm and

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making it scalable to high resolution

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videos it's always awesome to see

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different approaches i'm super excited

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to see the next paper improving upon

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this one also the results are still

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quite impressive and they could be used

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as a data augmentation tool for models

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working on videos due to their very low

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run time allowing other models to train

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on larger and more diverse data sets

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without much cost if you are interested

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in learning more about this technique i

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will strongly recommend reading their

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paper it is the first link in the

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description thank you for watching and

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to everyone supporting my work on

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patreon or by commenting and liking the

05:46

videos here on youtube

05:54

you

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