Muralikrishnan, S;
Chaudhuri, S;
Aigerman, N;
Kim, VG;
Fisher, M;
Mitra, NJ;
(2022)
GLASS: Geometric Latent Augmentation for Shape Spaces.
In:
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
(pp. pp. 18531-18540).
Institute of Electrical and Electronics Engineers (IEEE)
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Muralikrishnan_Glass_Geometric_Latent_Augmentation_for_Shape_Spaces_CVPR_2022_paper.pdf - Accepted Version Download (9MB) | Preview |
Abstract
We investigate the problem of training generative models on very sparse collections of 3D models. Particularly, instead of using difficult-to-obtain large sets of 3D models, we demonstrate that geometrically-motivated energy functions can be used to effectively augment and boost only a sparse collection of example (training) models. Technically, we analyze the Hessian of the as-rigid-as-possible (ARAP) energy to adaptively sample from and project to the underlying (local) shape space, and use the augmented dataset to train a variational autoencoder (VAE). We iterate the process, of building latent spaces of VAE and augmenting the associated dataset, to progressively reveal a richer and more expressive generative space for creating geometrically and semantically valid samples. We evaluate our method against a set of strong baselines, provide ablation studies, and demonstrate application towards establishing shape correspondences. Glassproduces multiple interesting and meaningful shape variations even when starting from as few as 3-10 training shapes. Our code is available at https://sanjeevmk.github.io/glass_webpage/.
| Type: | Proceedings paper |
|---|---|
| Title: | GLASS: Geometric Latent Augmentation for Shape Spaces |
| Event: | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Location: | New Orleans, LA, USA |
| Dates: | 18th-24th June 2022 |
| ISBN-13: | 9781665469463 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPR52688.2022.01800 |
| Publisher version: | https://doi.org/10.1109/CVPR52688.2022.01800 |
| 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. |
| UCL classification: | UCL 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/10162183 |
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