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About this paper
Hongsheng Wang, Lizao Zhang, Zhangnan Zhong, Shuolin Xu, Xinrui Zhou, Shengyu Zhang, Huahao Xu, Fei Wu and 1 more
RemoCap, from Zhejiang University and Zhejiang Lab, introduces a Transformer-based framework that employs Spatial and Motion Disentanglement Modules to reconstruct 3D human mesh sequences from monocular video, effectively addressing complex occlusions. It demonstrated leading performance on the 3DPW dataset, recording 81.9 mm MPVPE, 72.7 mm MPJPE, and 44.1 mm PA-MPJPE.
Key insights
- Explicitly disentangling spatial features (target vs. non-target objects) is crucial for accurate reconstruction under occlusion, as demonstrated by the SD module's contribution to PA-MPJPE improvements.
- Decoupling motion features from static elements, achieved by the MD module and a novel sequence velocity loss, helps capture consistent dynamics in highly mobile regions and mitigate motion feature coupling.
- The combined effect of both spatial and motion disentanglement modules, alongside temporal velocity constraints, provides a complementary approach that is essential for robustly handling complex occlusion scenarios.
Summary by alphaXiv.
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