Learning to Infer Semantic Parameters for 3D Shape Editing

Fangyin Wei, Elena Sizikova, Avneesh Sud, Szymon Rusinkiewicz, Thomas Funkhouser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e.g., the pose of a person's arm or the length of an airplane's wing) while preserving as much existing details as possible. We propose to learn a deep network that infers the semantic parameters of an input shape and then allows the user to manipulate those parameters. The network is trained jointly on shapes from an auxiliary synthetic template and unlabeled realistic models, ensuring robustness to shape variability while relieving the need to label realistic exemplars. At testing time, edits within the parameter space drive deformations to be applied to the original shape, which provides semantically-meaningful manipulation while preserving the details. This is in contrast to prior methods that either use autoencoders with a limited latent-space dimensionality, failing to preserve arbitrary detail, or drive deformations with purely-geometric controls, such as cages, losing the ability to update local part regions. Experiments with datasets of chairs, airplanes, and human bodies demonstrate that our method produces more natural edits than prior work.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 International Conference on 3D Vision, 3DV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Electronic)9781728181288
StatePublished - Nov 2020
Event8th International Conference on 3D Vision, 3DV 2020 - Virtual, Fukuoka, Japan
Duration: Nov 25 2020Nov 28 2020

Publication series

NameProceedings - 2020 International Conference on 3D Vision, 3DV 2020


Conference8th International Conference on 3D Vision, 3DV 2020
CityVirtual, Fukuoka

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition


  • 3D Shape Editing
  • Deep Learning


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