EditVAE: Unsupervised Parts-Aware Controllable 3D Point Cloud Shape Generation


  • Shidi Li The Australian National University
  • Miaomiao Liu The Australian National University
  • Christian Walder Data61, CSIRO The Australian National University




Computer Vision (CV)


This paper tackles the problem of parts-aware point cloud generation. Unlike existing works which require the point cloud to be segmented into parts a priori, our parts-aware editing and generation are performed in an unsupervised manner. We achieve this with a simple modification of the Variational Auto-Encoder which yields a joint model of the point cloud itself along with a schematic representation of it as a combination of shape primitives. In particular, we introduce a latent representation of the point cloud which can be decomposed into a disentangled representation for each part of the shape. These parts are in turn disentangled into both a shape primitive and a point cloud representation, along with a standardising transformation to a canonical coordinate system. The dependencies between our standardising transformations preserve the spatial dependencies between the parts in a manner that allows meaningful parts-aware point cloud generation and shape editing. In addition to the flexibility afforded by our disentangled representation, the inductive bias introduced by our joint modeling approach yields state-of-the-art experimental results on the ShapeNet dataset.




How to Cite

Li, S., Liu, M., & Walder, C. (2022). EditVAE: Unsupervised Parts-Aware Controllable 3D Point Cloud Shape Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 1386-1394. https://doi.org/10.1609/aaai.v36i2.20027



AAAI Technical Track on Computer Vision II