DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

• URL: http://arxiv.org/abs/2102.09105v1
• Date: Thu, 18 Feb 2021 01:31:26 GMT
• Title: DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
• Authors: Minghua Liu, Minhyuk Sung, Radomir Mech, Hao Su
• Abstract summary: DeepMetaHandles is a 3D conditional generative model based on mesh deformation. We learn a set of meta-handles for each shape, which are represented as combinations of the given handles. A new deformation can then be generated by sampling the coefficients of the meta-handles in a specific range.
• Score: 31.317344570058058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the coefficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points' translations to the entire mesh. To avoid learning zero deformation as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations' plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles.

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