- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
We propose a novel learnable representation for detail preserving shape deformation. The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source. Our method extends a traditional cage-based deformation technique, where the source shape is enclosed by a coarse control mesh termed cage, and translations prescribed on the cage vertices are interpolated to any point on the source mesh via special weight functions. The use of this sparse cage scaffolding enables preserving surface details regardless of the shape's intricacy and topology. Our key contribution is a novel neural network architecture for predicting deformations by controlling the cage. We incorporate a differentiable cage-based deformation module in our architecture, and train our network end-to-end. Our method can be trained with common collections of 3D models in an unsupervised fashion, without any cage-specific annotations. We demonstrate the utility of our method for synthesizing shape variations and deformation transfer. Show more
Book title2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Pages / Article No.
Organisational unit03911 - Sorkine Hornung, Olga / Sorkine Hornung, Olga
NotesConference lecture held on June 16, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.
MoreShow all metadata