Giebenhain, Simon;
Kirschstein, Tobias;
Georgopoulos, Markos;
Ruenz, Martin;
Agapito, Lourdes;
Niessnerl, Matthias;
(2024)
MonoNPHM: Dynamic Head Reconstruction from Monocular Videos.
In:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.
(pp. pp. 10747-10758).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos. To this end, we propose a latent appearance space that parameterizes a texture field on top of a neural parametric model. We constrain predicted color values to be correlated with the underlying geometry such that gradients from RGB effectively influence latent geometry codes during inverse rendering. To increase the representational capacity of our expression space, we augment our backward deformation field with hyper-dimensions, thus improving color and geometry representation in topologically challenging expressions. Using MonoNPHM as a learned prior, we approach the task of 3D head reconstruction using signed distance field based volumetric rendering. By numerically inverting our backward deformation field, we incorporated a landmark loss using facial anchor points that are closely tied to our canonical geometry representation. To evaluate the task of dynamic face reconstruction from monocular RGB videos we record 20 challenging Kinect sequences under casual conditions. MonoNPHM outper-forms all baselines with a significant margin, and makes an important step towards easily accessible neural parametric face models through RGB tracking.
| Type: | Proceedings paper |
|---|---|
| Title: | MonoNPHM: Dynamic Head Reconstruction from Monocular Videos |
| Event: | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Location: | Seattle, WA, USA |
| Dates: | 16th-22nd June 2024 |
| ISBN-13: | 979-8-3503-5300-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPR52733.2024.01022 |
| Publisher version: | https://doi.org/10.1109/cvpr52733.2024.01022 |
| 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 > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203952 |
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