It is now possible to capture time-varying 3D point clouds at high spatial and temporal resolution. This allows for high-quality acquisitions of human bodies and faces in motion. However, tools to process and analyze these data robustly and automatically are missing. Such tools are critical to learning generative models of human motion, which can be leveraged to create plausible synthetic human motion sequences. This has the potential to influence virtual reality applications such as virtual change rooms or crowd simulations. Developing such tools is challenging due to the high variability in human shape and motion and due to significant geometric and topological acquisition noise present in state-of-the-art acquisitions. The main objective of 3DMOVE is to automatically compute high-quality generative models from a database of raw dense 3D motion sequences for human bodies and faces. To achieve this objective, 3DMOVE will leverage recently developed deep learning techniques.
- Stefanie Wuhrer, Inria
- Mathieu Marsot (Ph.D. student at Université Grenoble Alpes since Nov. 2019, co-advised by Jean-Sébastien Franco and Stefanie Wuhrer)
- Raphaël Dang-Nhu (Ph.D. student at Université Grenoble Alpes since Oct. 2020, co-advised by Anne-Hélène Olivier and Stefanie Wuhrer)
Financing period: 2019 – 2024
Financing agency: ANR