# RopeTP: Global Human Motion Recovery via Integrating Robust Pose Estimation with Diffusion Trajectory Prior

arXiv: https://arxiv.org/abs/2410.20358 · HTML: https://mlsanity.com/paper/2410.20358/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 27 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
- [SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark](https://mlsanity.com/paper/2310.20436/) (2023)
- [ReinDiffuse: Crafting Physically Plausible Motions with Reinforced Diffusion Model](https://mlsanity.com/paper/2410.07296/) (2024)
- [ReBaR: Reference-Based Reasoning for Robust Pose Estimation from Monocular Images](https://mlsanity.com/paper/2303.11675/) (2023)

## Built on by (most cited first, 2 of 2)

- [DIDiffGes: Decoupled Semi-Implicit Diffusion Models for Real-time Gesture Generation from Speech](https://mlsanity.com/paper/2503.17059/) (2025)
- [Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication](https://mlsanity.com/paper/2505.04996/) (2025)

## Abstract (arXiv)

We present RopeTP, a novel framework that combines Robust pose estimation with a diffusion Trajectory Prior to reconstruct global human motion from videos. At the heart of RopeTP is a hierarchical attention mechanism that significantly improves context awareness, which is essential for accurately inferring the posture of occluded body parts. This is achieved by exploiting the relationships with visible anatomical structures, enhancing the accuracy of local pose estimations. The improved robustness of these local estimations allows for the reconstruction of precise and stable global trajectories. Additionally, RopeTP incorporates a diffusion trajectory model that predicts realistic human motion from local pose sequences. This model ensures that the generated trajectories are not only consistent with observed local actions but also unfold naturally over time, thereby improving the realism and stability of 3D human motion reconstruction. Extensive experimental validation shows that RopeTP surpasses current methods on two benchmark datasets, particularly excelling in scenarios with occlusions. It also outperforms methods that rely on SLAM for initial camera estimates and extensive optimization, delivering more accurate and realistic trajectories.