Summary of Wf-vae: Enhancing Video Vae by Wavelet-driven Energy Flow For Latent Video Diffusion Model, By Zongjian Li and Bin Lin and Yang Ye and Liuhan Chen and Xinhua Cheng and Shenghai Yuan and Li Yuan
WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model
by Zongjian Li, Bin Lin, Yang Ye, Liuhan Chen, Xinhua Cheng, Shenghai Yuan, Li Yuan
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, the authors propose a novel Video Variational Autoencoder (VAE) called Wavelet Flow VAE (WF-VAE), which leverages multi-level wavelet transform to efficiently encode videos into a low-dimensional latent space. This approach addresses the computational bottleneck of traditional video VAEs, allowing for faster training and lower memory consumption while maintaining competitive reconstruction quality. The authors also introduce Causal Cache, a method that maintains the integrity of the latent space during block-wise inference. Experimental results show that WF-VAE outperforms state-of-the-art video VAEs in terms of both PSNR and LPIPS metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to compress videos using Wavelet Flow VAE (WF-VAE). This is important because it makes it easier and faster to train machines that can generate videos. The authors also developed a method called Causal Cache, which helps keep the information in the video consistent when processing long videos. WF-VAE performs better than other video compression methods and uses less memory and computing power. |
Keywords
» Artificial intelligence » Inference » Latent space » Variational autoencoder
