RoHM: Robust Human Motion Reconstruction via Diffusion

• URL: http://arxiv.org/abs/2401.08570v2
• Date: Mon, 15 Apr 2024 12:27:13 GMT
• Title: RoHM: Robust Human Motion Reconstruction via Diffusion
• Authors: Siwei Zhang, Bharat Lal Bhatnagar, Yuanlu Xu, Alexander Winkler, Petr Kadlecek, Siyu Tang, Federica Bogo,
• Abstract summary: RoHM is an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos. It conditioned on noisy and occluded input data, reconstructs complete, plausible motions in consistent global coordinates. Our method outperforms state-of-the-art approaches qualitatively and quantitatively, while being faster at test time.
• Score: 58.63706638272891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose RoHM, an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos in the presence of noise and occlusions. Most previous approaches either train neural networks to directly regress motion in 3D or learn data-driven motion priors and combine them with optimization at test time. The former do not recover globally coherent motion and fail under occlusions; the latter are time-consuming, prone to local minima, and require manual tuning. To overcome these shortcomings, we exploit the iterative, denoising nature of diffusion models. RoHM is a novel diffusion-based motion model that, conditioned on noisy and occluded input data, reconstructs complete, plausible motions in consistent global coordinates. Given the complexity of the problem -- requiring one to address different tasks (denoising and infilling) in different solution spaces (local and global motion) -- we decompose it into two sub-tasks and learn two models, one for global trajectory and one for local motion. To capture the correlations between the two, we then introduce a novel conditioning module, combining it with an iterative inference scheme. We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling. Extensive experiments on three popular datasets show that our method outperforms state-of-the-art approaches qualitatively and quantitatively, while being faster at test time. The code is available at https://sanweiliti.github.io/ROHM/ROHM.html.

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