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Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation.
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Deformation transfer for triangle meshes
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Non-homogeneous resizing of complex models
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Semantic deformation transfer
Ilya Baran, Daniel Vlasic, Eitan Grinspun, and Jovan Popović · 2009
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Spatial deformation transfer
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Generative adversarial nets
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Shapenet: An information-rich 3d model repository
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Realtime style transfer for unlabeled heterogeneous human motion
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Functional characterization of intrinsic and extrinsic geometry
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