BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
Nov 30, 2023·
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1 min read
Georgios Albanis
![Nikolaos Zioulis](/author/nikolaos-zioulis/avatar_hubb9eb952d7ba657df91a885eee34648c_440135_50x50_fill_q80_h2_lanczos_center_3.webp)
Nikolaos Zioulis
Kostas Kolomvatsos
![](/publication/bundle/featured_huc8a7d0e6973758be14c02a75a43041ad_582232_6608808f357a9e71cdaef526ec1c1c65.webp)
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.
Type
Publication
In Proceedings of the 20th ACM SIGGRAPH European Conference on Visual Media Production
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