Summary of Cv-vae: a Compatible Video Vae For Latent Generative Video Models, by Sijie Zhao et al.
CV-VAE: A Compatible Video VAE for Latent Generative Video Models
by Sijie Zhao, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Muyao Niu, Xiaoyu Li, Wenbo Hu, Ying Shan
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 method for training a continuous video Variational Autoencoder (CV-VAE) that is compatible with image-based latent diffusion models, such as Stable Diffusion (SD). The CV-VAE is designed to address the issue of a latent space gap between video and image-based models. This is achieved through a novel latent space regularization loss function, which utilizes an image VAE to regularize the training process. The proposed method enables seamless training of video models from pre-trained T2I or video models in a spatio-temporally compressed latent space. Experimental results demonstrate the effectiveness of the CV-VAE, allowing for four times more frames to be generated with minimal finetuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a new type of computer model that can compress and process videos in a way that makes it easier for other models to understand and work with video data. Right now, there isn’t a good way to make this happen, so the researchers came up with a new approach called CV-VAE (Continuous Video Variational Autoencoder). This method helps different video and image-based models talk to each other better by making sure they have the same underlying structure or “language”. By doing this, the paper shows that it’s possible to train video models from pre-trained text-to-image models with much less effort. The results are promising, allowing for four times more frames to be generated without needing a lot of extra training. |
Keywords
» Artificial intelligence » Diffusion » Latent space » Loss function » Regularization » Variational autoencoder
