The chair aims to investigate deep learning for 3D computer vision in order to overcome some of the limitations in this field. Applications are particularly focused on humans and the ability to capture and analyze their shapes, appearances, and movements, for upcoming new media devices, sports, and medical applications.
SELECTED LIST OF PUBLICATIONS
Vincent Leroy, Jean-Sébastien Franco, Edmond Boyer. “Volume Sweeping: Learning Photoconsistency for Multi-View Shape Reconstruction.” International Journal of Computer Vision, Springer Verlag, 2021, vol. 129, pp. 284–299.
Jean Basset, Adnane Boukhayma, Stefanie Wuhrer, Franck Multon, Edmond Boyer. Neural Human Deformation Transfer. International Conference on 3D Vision, Dec. 2021, London, United Kingdom
Pierre Zins, Yuanlu Xu, Edmond Boyer, Stefanie Wuhrer, Tony Tung. Data-Driven 3D Reconstruction of Clothed Humans From Sparse Views. 3DV 2021 - International Conference on 3D Vision, Dec. 2021, London, United Kingdom.
Adaptive Mesh Texture for Multi-View Appearance Modeling. Matthieu Armando, Jean-Sébastien Franco, Edmond Boyer. 3DV 2019 - 7th International Conference on 3D Vision, September 2019, Quebec City, Canada.
"Reconstructing Human Body Mesh from Point Clouds Using an Adversarial GP Network" by Boyao Zhou, Jean-Sébastien Franco, Federica Bogo, Bugra Tekin, and Edmond Boyer; ACCV 2020, Asian Conference on Computer Vision, Kyoto, Japan.
Published on October 14, 2026 Updated on October 14, 2026
3D face modeling
3D reconstruction from a single or a few images
Spine modeling using multiple sensor modalities
Dynamic models of humans
3D modeling from partial observations
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