Mohamed, M;
Agapito, L;
(2023)
GNPM: Geometric-Aware Neural Parametric Models.
In:
Proceedings of 2022 International Conference on 3D Vision, 3DV 2022.
(pp. pp. 166-175).
IEEE: Prague, Czech Republic.
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Abstract
We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into account the local structure of data to learn disentangled shape and pose latent spaces of 4D dynamics, using a geometric-aware architecture on point clouds. Temporally consistent 3D deformations are estimated without the need for dense correspondences at training time, by exploiting cycle consistency. Besides its ability to learn dense correspondences, GNPMs also enable latent-space manipulations such as interpolation and shape/pose transfer. We evaluate GNPMs on various datasets of clothed humans, and show that it achieves comparable performance to state of the art methods that require dense correspondences during training.
| Type: | Proceedings paper |
|---|---|
| Title: | GNPM: Geometric-Aware Neural Parametric Models |
| Event: | 2022 International Conference on 3D Vision (3DV) |
| Dates: | 12 Sep 2022 - 16 Sep 2022 |
| ISBN-13: | 9781665456708 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/3DV57658.2022.00029 |
| Publisher version: | https://doi.org/10.1109/3DV57658.2022.00029 |
| 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: | Training, Point cloud compression, Interpolation, Image segmentation, Three-dimensional displays, Shape, Deformation |
| 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-pp.ucl.ac.uk/id/eprint/10168553 |
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