RoHM: Robust Human Motion Reconstruction via Diffusion
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Date
2024
Publication Type
Conference Paper
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yes
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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 co-ordinates. 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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published
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Book title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Pages / Article No.
14606 - 14617
Publisher
IEEE
Event
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
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Subject
3D human pose estimation; Motion capture; Motion reconstruction; Motion prior; Human mesh recovery; Diffusion
Organisational unit
09686 - Tang, Siyu / Tang, Siyu
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Related publications and datasets
Is supplemented by: https://sanweiliti.github.io/ROHM/ROHM.html