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Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling
[article]
2019
arXiv
pre-print
Data-driven modeling of human motions is ubiquitous in computer graphics and computer vision applications, such as synthesizing realistic motions or recognizing actions. Recent research has shown that such problems can be approached by learning a natural motion manifold using deep learning to address the shortcomings of traditional data-driven approaches. However, previous methods can be sub-optimal for two reasons. First, the skeletal information has not been fully utilized for feature
arXiv:1908.07214v1
fatcat:cbvk7xhpujeszcm6rapoqg2nzi