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Self-supervised Neural Articulated Shape and Appearance Models
[article]
2022
arXiv
pre-print
Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches has focused on static objects, dynamic objects, especially with controllable articulation, are less explored. We propose a novel approach for learning a representation of the geometry, appearance, and motion of a class of articulated objects given only a set of color images as input. In a self-supervised manner, our
arXiv:2205.08525v1
fatcat:fjmtfkjn7jbkzafuncxzsu5lje