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Many applications in 3D shape design and augmentation require the ability tomake specific edits to an object's semantic parameters (e.g., the pose of aperson's arm or the length of an airplane's wing) while preserving as muchexisting details as possible. We propose to learn a deep network that infersthe semantic parameters of an input shape and then allows the user tomanipulate those parameters. The network is trained jointly on shapes from anauxiliary synthetic template and unlabeled realistic models, ensuringrobustness to shape variability while relieving the need to label realisticexemplars. At testing time, edits within the parameter space drive deformationsto be applied to the original shape, which provides semantically-meaningfulmanipulation while preserving the details. This is in contrast to prior methodsthat either use autoencoders with a limited latent-space dimensionality,failing to preserve arbitrary detail, or drive deformations withpurely-geometric controls, such as cages, losing the ability to update localpart regions. Experiments with datasets of chairs, airplanes, and human bodiesdemonstrate that our method produces more natural edits than prior work.

Learning to Infer Semantic Parameters for 3D Shape Editing