Summary of Track4gen: Teaching Video Diffusion Models to Track Points Improves Video Generation, by Hyeonho Jeong et al.
Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation
by Hyeonho Jeong, Chun-Hao Paul Huang, Jong Chul Ye, Niloy Mitra, Duygu Ceylan
First submitted to arxiv on: 8 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 Track4Gen model addresses the issue of appearance drift in video generators by incorporating point tracking across frames. By combining video diffusion loss with spatial supervision, Track4Gen enhances the features generated during the video generation process. This unification of tasks is achieved through minimal modifications to existing video generation architectures. The evaluation results demonstrate the effectiveness of Track4Gen in reducing appearance drift and producing temporally stable and visually coherent videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Track4Gen is a new way to make videos that don’t change too much over time. Right now, computers can generate videos, but they often look weird because things move or change suddenly. The problem is that these video generators don’t know what’s happening in each frame of the video. Track4Gen fixes this by tracking specific points in each frame and making sure they’re consistent throughout the video. This makes the generated videos more realistic and stable. |
Keywords
» Artificial intelligence » Diffusion » Tracking
