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

被引:0
|
作者
Albanis, Georgios [1 ,2 ]
Zioulis, Nikolaos [2 ]
Kolomvatsos, Kostas [1 ]
机构
[1] Univ Thessaly, Dept Informat & Telecommun, Lamia, Greece
[2] Moverse, Thessaloniki, Greece
关键词
Motion Capture; MoCap; Representation Learning; Markerless Motion Capture; Human Body Pose and Shape Fitting; Bundle Solving; Latent Interpolation;
D O I
10.1145/3626495.3626511
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页数:9
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