From motion capture data to character animation
Proceedings of the ACM symposium on Virtual reality software and technology
Model-Based Tracking by Classification in a Tiny Discrete Pose Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Articulated motion reconstruction from feature points
Pattern Recognition
A two-stage dynamic model for visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We propose a general algorithm for identifying an arbitrary pose of an articulated subject with sparse point features. The algorithm aims to identify a one-to-one correspondence between a model point-set and an observed point-set taken from freeform motion of the articulated subject. We avoid common assumptions such as pose similarity or small motions with respect to the model, and assume no prior knowledge from which to infer an initial or partial correspondence between the two point-sets. The algorithm integrates local segment-based correspondences under a set of affine transformations, and a global hierarchical search strategy. Experimental results, based on synthetic pose and real-world human motion data demonstrate the ability of the algorithm to perform the identification task. Reliability is increasingly compromised with increasing data noise and segmental distortion, but the algorithm can tolerate moderate levels. This work contributes to establishing a crucial self-initializing identification in model-based point-feature tracking for articulated motion.