Summary of S2dm: Sector-shaped Diffusion Models For Video Generation, by Haoran Lang et al.
S2DM: Sector-Shaped Diffusion Models for Video Generation
by Haoran Lang, Yuxuan Ge, Zheng Tian
First submitted to arxiv on: 20 Mar 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 This paper proposes a novel diffusion model, Sector-Shaped Diffusion Model (S2DM), to generate videos while maintaining consistency and continuity across frames. The S2DM framework leverages ray-shaped reverse diffusion processes to form a sector-shaped diffusion region, allowing it to generate data sharing the same semantic and stochastic features while varying on temporal features with guided conditions. The authors apply S2DM to video generation tasks and explore the use of optical flow as temporal conditions, achieving state-of-the-art results without explicit temporal-feature modeling modules. For text-to-video generation tasks, a two-stage generation strategy is proposed to decouple temporal feature generation from semantic content features, demonstrating comparable performance with existing works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to generate videos that looks realistic and consistent. The problem is that current methods struggle to create smooth transitions between frames in a video. To solve this, the authors propose a new model called S2DM that can generate videos with different scenes while keeping them connected. They test their model on various tasks and show that it outperforms other models without needing additional training data. The potential applications of this technology are vast, including creating realistic movies and TV shows, as well as enhancing the visual effects in video games. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Optical flow
