Geometric deep learning for non-rigid shapes: From Theory to Practice
Date:
The analysis of 3D human bodies is a fundamental problem with applications in healthcare, virtual reality, animation, and motion understanding. We introduce the main challenges of representing human shape and pose, as well as the need for geometric invariances when building shape spaces. We review classical approaches, from handcrafted descriptors, statistical body models such as SMPL to Riemannian shape analysis. In a second part, we introduce deep learning methods for 3D data such as PointNet and mesh-based CNNs. We also introduce recent techniques to build a disentangled latent representations for human shape and pose. We conclude the talk by presenting our recent learned Riemannian approach to overcome current limitations of deep learning for 3D human body analysis.