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

Minghua Liu, Minhyuk Sung, Radomir Mech, Hao Su; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 12-21

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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Liu_2021_CVPR, author = {Liu, Minghua and Sung, Minhyuk and Mech, Radomir and Su, Hao}, title = {DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes With Biharmonic Coordinates}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {12-21} }