Sung, M;
Jiang, Z;
Achlioptas, P;
Mitra, NJ;
Guibas, LJ;
(2020)
DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces.
ACM Transactions on Graphics
, 39
(6)
, Article 261. 10.1145/3414685.3417783.
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Abstract
Shape deformation is an important component in any geometry processing toolbox. The goal is to enable intuitive deformations of single or multiple shapes or to transfer example deformations to new shapes while preserving the plausibility of the deformed shape(s). Existing approaches assume access to point-level or part-level correspondence or establish them in a preprocessing phase, thus limiting the scope and generality of such approaches. We propose DeformSyncNet, a new approach that allows consistent and synchronized shape deformations without requiring explicit correspondence information. Technically, we achieve this by encoding deformations into a class-specific idealized latent space while decoding them into an individual, model-specific linear deformation action space, operating directly in 3D. The underlying encoding and decoding are performed by specialized (jointly trained) neural networks. By design, the inductive bias of our networks results in a deformation space with several desirable properties, such as path invariance across different deformation pathways, which are then also approximately preserved in real space. We qualitatively and quantitatively evaluate our framework against multiple alternative approaches and demonstrate improved performance.
| Type: | Article |
|---|---|
| Title: | DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3414685.3417783 |
| Publisher version: | https://doi.org/10.1145/3414685.3417783 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Computing methodologies, Shape modeling, Shape analysis, Machine learning approaches |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10122577 |
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