Summary of Coin: Control-inpainting Diffusion Prior For Human and Camera Motion Estimation, by Jiefeng Li et al.
COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation
by Jiefeng Li, Ye Yuan, Davis Rempe, Haotian Zhang, Pavlo Molchanov, Cewu Lu, Jan Kautz, Umar Iqbal
First submitted to arxiv on: 29 Aug 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 The proposed control-inpainting motion diffusion prior, COIN, enables fine-grained control to disentangle human and camera motions from RGB videos. This approach leverages pre-trained motion diffusion models to encode rich motion priors, but also introduces a novel control-inpainting score distillation sampling method to ensure well-aligned, consistent, and high-quality motion estimation within a joint optimization framework. Additionally, COIN incorporates a new human-scene relation loss to alleviate the scale ambiguity by enforcing consistency among humans, camera, and scene. The approach is evaluated on three challenging benchmarks, demonstrating state-of-the-art performance in global human motion estimation and camera motion estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what people are doing in a video from different angles. It’s like trying to see through moving clouds! To make it easier, researchers developed a new method called COIN that separates the movements of people and cameras. This helps create more accurate and detailed videos of human motions. They tested this on several challenging datasets and found that their approach outperformed others by 33%! |
Keywords
» Artificial intelligence » Diffusion » Distillation » Optimization
