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arXiv · 2311.12679

BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos

Georgios Albanis·Nikolaos Zioulis·Kostas Kolomvatsos
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Abstract

Capturing smooth motions from videos using markerless techniques typically involves complex processes such as temporal constraints, multiple stages with data-driven regression and optimization, and bundle solving over temporal windows. These processes can be inefficient and require tuning multiple objectives across stages. In contrast, BundleMoCap introduces a novel and efficient approach to this problem. It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions. BundleMoCap outperforms the state-of-the-art without increasing complexity. The key concept behind BundleMoCap is manifold interpolation between latent keyframes. By relying on a local manifold smoothness assumption, we can efficiently solve a bundle of frames using a single code. Additionally, the method can be implemented as a sliding window optimization and requires only the first frame to be properly initialized, reducing the overall computational burden. BundleMoCap's strength lies in its ability to achieve high-quality motion capture results with simplicity and efficiency. More details can be found at https://moverseai.github.io/bundle/.

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Georgios Albanis, Nikolaos Zioulis, Kostas Kolomvatsos. 2023-11-21. BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos. https://doi.org/10.1145/3626495.3626511

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