  In this paper, we propose a solution to the task of generating dynamic 3D
facial expressions from a neutral 3D face and an expression label. This
involves solving two sub-problems: (i)modeling the temporal dynamics of
expressions, and (ii) deforming the neutral mesh to obtain the expressive
counterpart. We represent the temporal evolution of expressions using the
motion of a sparse set of 3D landmarks that we learn to generate by training a
manifold-valued GAN (Motion3DGAN). To better encode the expression-induced
deformation and disentangle it from the identity information, the generated
motion is represented as per-frame displacement from a neutral configuration.
To generate the expressive meshes, we train a Sparse2Dense mesh Decoder
(S2D-Dec) that maps the landmark displacements to a dense, per-vertex
displacement. This allows us to learn how the motion of a sparse set of
landmarks influences the deformation of the overall face surface, independently
from the identity. Experimental results on the CoMA and D3DFACS datasets show
that our solution brings significant improvements with respect to previous
solutions in terms of both dynamic expression generation and mesh
reconstruction, while retaining good generalization to unseen data. The code
and the pretrained model will be made publicly available.

Computer Vision and Pattern Recognition;Artificial Intelligence