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This paper addresses 3D shape recognition. Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a uniform 3D grid centered on the shape, and resorts to deep convolutional neural networks (CNNs) for modeling these binary variables. Robust learning of such CNNs is currently limited by the small datasets of 3D shapes available - an order of magnitude...
This paper addresses the problem of modeling long-range motion patterns of a 3D human skeleton performing an activity. This problem is important, as such a model can be used in many applications, including person tracking via 3D pose estimation, and probabilistic sampling of realistic 3D skeleton sequences conducting different activities with different motion styles. To this end, we formulate a new...
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