In this work, we address the problem of 4D facial expressions generation.
This is usually addressed by animating a neutral 3D face to reach an expression
peak, and then get back to the neutral state. In the real world though, people
show more complex expressions, and switch from one expression to another. We
thus propose a new model that generates transitions between different
expressions, and synthesizes long and composed 4D expressions. This involves
three sub-problems: (i) modeling the temporal dynamics of expressions, (ii)
learning transitions between them, and (iii) deforming a generic mesh. We
propose to encode the temporal evolution of expressions using the motion of a
set of 3D landmarks, that we learn to generate by training a manifold-valued
GAN (Motion3DGAN). To allow the generation of composed expressions, this model
accepts two labels encoding the starting and the ending expressions. The final
sequence of meshes is generated by a Sparse2Dense mesh Decoder (S2D-Dec) that
maps the landmark displacements to a dense, per-vertex displacement of a known
mesh topology. By explicitly working with motion trajectories, the model is
totally independent from the identity. Extensive experiments on five public
datasets show that our proposed approach brings significant improvements with
respect to previous solutions, while retaining good generalization to unseen
data.

本文提出了一种新模型，使用 Motion3DGAN 模型编码表情的时间演变，并利用 Sparse2Dense mesh Decoder 模型生成可以复合的表情转换；在多个公共数据集上进行实验验证，表明该方法在保持良好泛化性能的同时相较于之前的解决方案有了显著提高。