Summary of Ropetp: Global Human Motion Recovery Via Integrating Robust Pose Estimation with Diffusion Trajectory Prior, by Mingjiang Liang et al.
RopeTP: Global Human Motion Recovery via Integrating Robust Pose Estimation with Diffusion Trajectory Prior
by Mingjiang Liang, Yongkang Cheng, Hualin Liang, Shaoli Huang, Wei Liu
First submitted to arxiv on: 27 Oct 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 paper introduces RopeTP, a novel framework combining robust pose estimation and trajectory prior to reconstruct global human motion from videos. RopeTP utilizes a hierarchical attention mechanism to enhance context awareness, crucial for accurately inferring occluded body parts. This is achieved by leveraging relationships with visible anatomical structures, improving local pose estimations. The improved robustness of these local estimations enables the reconstruction of precise and stable global trajectories. Additionally, RopeTP incorporates a diffusion trajectory model predicting realistic human motion from local pose sequences. This model ensures generated trajectories are consistent with observed local actions and unfold naturally over time, enhancing realism and stability of 3D human motion reconstruction. Experimental validation demonstrates RopeTP outperforms current methods on two benchmark datasets, particularly exceling in scenarios with occlusions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer program that can analyze videos and reconstruct what people are doing in 3D. This paper introduces a new way to do this called RopeTP. It’s good at figuring out what people are doing even when some parts of their bodies are hidden. This is important for making the reconstruction more accurate. The program uses a special technique called attention mechanism that helps it focus on the right things in the video. It also predicts how people will move over time, making the reconstruction look more natural. In tests, RopeTP did better than other programs at this task. |
Keywords
» Artificial intelligence » Attention » Diffusion » Pose estimation
