MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement

• URL: http://arxiv.org/abs/2007.12944v1
• Date: Sat, 25 Jul 2020 14:41:51 GMT
• Title: MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
• Authors: Rinon Gal, Amit Bermano, Hao Zhang, Daniel Cohen-Or
• Abstract summary: We present MRGAN, a multi-rooted adversarial network which generates part-disentangled 3D point-cloud shapes without part-based shape supervision. The network fuses multiple branches of tree-structured graph convolution layers which produce point clouds, with learnable constant inputs at the tree roots.
• Score: 49.05682172235875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present MRGAN, a multi-rooted adversarial network which generates part-disentangled 3D point-cloud shapes without part-based shape supervision. The network fuses multiple branches of tree-structured graph convolution layers which produce point clouds, with learnable constant inputs at the tree roots. Each branch learns to grow a different shape part, offering control over the shape generation at the part level. Our network encourages disentangled generation of semantic parts via two key ingredients: a root-mixing training strategy which helps decorrelate the different branches to facilitate disentanglement, and a set of loss terms designed with part disentanglement and shape semantics in mind. Of these, a novel convexity loss incentivizes the generation of parts that are more convex, as semantic parts tend to be. In addition, a root-dropping loss further ensures that each root seeds a single part, preventing the degeneration or over-growth of the point-producing branches. We evaluate the performance of our network on a number of 3D shape classes, and offer qualitative and quantitative comparisons to previous works and baseline approaches. We demonstrate the controllability offered by our part-disentangled generation through two applications for shape modeling: part mixing and individual part variation, without receiving segmented shapes as input.

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