Summary of Pyramidal Flow Matching For Efficient Video Generative Modeling, by Yang Jin et al.
Pyramidal Flow Matching for Efficient Video Generative Modeling
by Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song, Yadong Mu, Zhouchen Lin
First submitted to arxiv on: 8 Oct 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 proposed unified pyramidal flow matching algorithm reduces the complexity of video generation by reinterpretating the denoising trajectory as a series of pyramid stages, enabling more efficient modeling. The framework combines autoregressive video generation with a temporal pyramid to compress the full-resolution history. This allows for end-to-end optimization and single unified Diffusion Transformer (DiT) training. Experimental results demonstrate high-quality 5-second (up to 10-second) videos at 768p resolution and 24 FPS, trained within 20.7k A100 GPU hours. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video generation is a complex task that requires modeling a vast spatiotemporal space. To make it more manageable, some approaches use separate optimization of each sub-stage, which hinders knowledge sharing and flexibility. This new method introduces a pyramidal flow matching algorithm that reduces complexity by reinterpreting the denoising trajectory as a series of pyramid stages. This makes training more efficient and allows for high-quality video generation at 768p resolution and 24 FPS. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Optimization » Spatiotemporal » Transformer
