@misc{nocentini2024fixedtopologiesunregisteredtraining,title={Beyond Fixed Topologies: Unregistered Training and Comprehensive Evaluation Metrics for 3D Talking Heads},author={Nocentini, Federico and Besnier, Thomas and Ferrari, Claudio and Arguillere, Sylvain and Berretti, Stefano and Daoudi, Mohamed},year={2024},eprint={2410.11041},archiveprefix={arXiv},primaryclass={cs.CV},url={https://arxiv.org/abs/2410.11041},category={preprints}}
Preprint
Partial Non-rigid Deformations and interpolations of Human Body Surfaces
Thomas Besnier, Emery Pierson, Sylvain Arguillere, and 1 more author
@misc{besnier2024partialnonrigiddeformationsinterpolations,title={Partial Non-rigid Deformations and interpolations of Human Body Surfaces},author={Besnier, Thomas and Pierson, Emery and Arguillere, Sylvain and Daoudi, Mohamed},year={2024},eprint={2412.02306},archiveprefix={arXiv},primaryclass={cs.CV},url={https://arxiv.org/abs/2412.02306},category={preprints}}
Conference
ScanTalk: 3D Talking Heads from Unregistered Scans
Federico Nocentini, Thomas Besnier, Claudio Ferrari, and 3 more authors
@misc{nocentini2024scantalk3dtalkingheads,title={ScanTalk: 3D Talking Heads from Unregistered Scans},author={Nocentini, Federico and Besnier, Thomas and Ferrari, Claudio and Arguillere, Sylvain and Berretti, Stefano and Daoudi, Mohamed},year={2024},booktitle={Proceedings of the IEEE/CVF European Conference on Computer Vision (ECCV)},eprint={2403.10942},archiveprefix={arXiv},primaryclass={cs.CV},category={conference-papers}}
2023
Journal
Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures
Thomas Besnier, Sylvain Arguillère, Emery Pierson, and 1 more author
3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. However, the acquired data is often inconsistent, resulting in unregistered meshes or point clouds. Many generative learning algorithms require correspondence between each point when comparing the predicted shape and the target shape. We propose an architecture able to cope with different parameterizations, even during the training phase. In particular, our loss function is built upon a kernel-based metric over a representation of meshes using geometric measures such as currents and varifolds. The latter allows to implement an efficient dissimilarity measure with many desirable properties such as robustness to resampling of the mesh or point cloud. We demonstrate the efficiency and resilience of our model with a generative learning task of human faces.
@article{BESNIER2023,title={Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures},journal={Computers & Graphics},volume={115},pages={309-320},year={2023},issn={0097-8493},doi={https://doi.org/10.1016/j.cag.2023.06.027},url={https://www.sciencedirect.com/science/article/pii/S009784932300122X},author={Besnier, Thomas and Arguillère, Sylvain and Pierson, Emery and Daoudi, Mohamed},category={journal-articles},keywords={3D generative model, Unsupervised learning, Geometric measures}}
Conference
A Function Space Perspective on Stochastic Shape Evolution
Elizabeth Baker, Thomas Besnier, and Stefan Sommer
Modelling randomness in shape data, for example, the evolution of shapes of organisms in biology, requires stochastic models of shapes. This paper presents a new stochastic shape model based on a description of shapes as functions in a Sobolev space. Using an explicit orthonormal basis as a reference frame for the noise, the model is independent of the parameterisation of the mesh. We define the stochastic model, explore its properties, and illustrate examples of stochastic shape evolutions using the resulting numerical framework.
@inproceedings{Baker_Besnier_2022,author={Baker, Elizabeth and Besnier, Thomas and Sommer, Stefan},editor={Gade, Rikke and Felsberg, Michael and K{\"a}m{\"a}r{\"a}inen, Joni-Kristian},title={A Function Space Perspective on Stochastic Shape Evolution},booktitle={Image Analysis},year={2023},publisher={Springer Nature Switzerland},address={Cham},pages={278--292},isbn={978-3-031-31438-4},category={conference-papers}}
