Summary of Efficient Video Diffusion Models Via Content-frame Motion-latent Decomposition, by Sihyun Yu et al.
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
by Sihyun Yu, Weili Nie, De-An Huang, Boyi Li, Jinwoo Shin, Anima Anandkumar
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel efficient extension to video diffusion models called Content-Motion Latent Diffusion (CMD). Current video diffusion models process high-dimensional videos directly, leading to high memory and computational requirements. To address this issue, CMD uses an autoencoder that encodes a video as a combination of a content frame and a low-dimensional motion latent representation. The former represents the common content, and the latter represents the underlying motion in the video. The paper generates the content frame by fine-tuning a pretrained image diffusion model, and the motion latent representation by training a new lightweight diffusion model. A key innovation is the design of a compact latent space that can directly utilize a pretrained image diffusion model. This leads to better quality generation and reduced computational costs. For instance, CMD can sample a video 7.7 times faster than prior approaches. Additionally, CMD achieves an FVD score of 212.7 on WebVid-10M, which is 27.3% better than the previous state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it easier to generate videos using computer models. Currently, these models are very good at generating images, but they struggle when trying to create videos. The problem is that videos are much bigger and more complicated than images. To fix this, the researchers created a new way of breaking down videos into smaller parts. They used an old model that was great at making images and combined it with a new model that could understand how the video moved. This new approach makes generating videos faster and better quality. It can even make a 16-second video in just 3 seconds! The researchers also tested their method on a large database of videos and found that it worked really well. |
Keywords
* Artificial intelligence * Autoencoder * Diffusion * Diffusion model * Fine tuning * Latent space
