Summary of Videodpo: Omni-preference Alignment For Video Diffusion Generation, by Runtao Liu et al.
VideoDPO: Omni-Preference Alignment for Video Diffusion Generation
by Runtao Liu, Haoyu Wu, Zheng Ziqiang, Chen Wei, Yingqing He, Renjie Pi, Qifeng Chen
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 VideoDPO pipeline pioneers the adaptation of Direct Preference Optimization (DPO) to video diffusion models. By comprehensively considering both visual quality and semantic alignment between text and videos, the OmniScore preference score is constructed. A pipeline is designed to automatically collect preference pair data based on this score, which significantly impacts overall preference alignment. Experiments demonstrate substantial improvements in both visual quality and semantic alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a big step forward in making video generation more personalized to users’ preferences. Right now, computer-generated videos don’t always match what people want. The authors try to fix this by using a special method called Direct Preference Optimization (DPO). They make some changes to DPO so it works better for videos than just pictures or words. This helps the computer understand what’s important – both how good the video looks and whether it matches what was written about it. The results are really good, making more personalized videos that users will like. |
Keywords
» Artificial intelligence » Alignment » Optimization
