Joint-dependent local deformations for hand animation and object grasping
Proceedings on Graphics interface '88
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photorealistic Scene Reconstruction by Voxel Coloring
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Surface Capture for Performance-Based Animation
IEEE Computer Graphics and Applications
Performance capture from sparse multi-view video
ACM SIGGRAPH 2008 papers
Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation
International Journal of Computer Vision
International Journal of Computer Vision
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
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The major issue in markerless motion capture is finding the global optimum from the multimodal setting where distinctive gestures may have similar likelihood values. Instead of only focusing on effective searching as many existing works, our approach resolves gesture ambiguity by designing a better-behaved observation likelihood. We extend Annealed Particle Filtering by a novel gradual sampling scheme that allows evaluations to concentrate on large mismatches of the tracking subject. Noticing the limitation of silhouettes in resolving gesture ambiguity, we incorporate appearance information in an illumination invariant way by maximising Mutual Information between an appearance model and the observation. This in turn strengthens the effectiveness of the better-behaved likelihood. Experiments on the benchmark datasets show that our tracking performance is comparable to or higher than the state-of-the-art studies, but with simpler setting and higher computational efficiency.