Realistic speech-driven 3D facial animation is a challenging problem due to
the complex relationship between speech and face. In this paper, we propose a
deep architecture, called Geometry-guided Dense Perspective Network (GDPnet),
to achieve speaker-independent realistic 3D facial animation. The encoder is
designed with dense connections to strengthen feature propagation and encourage
the re-use of audio features, and the decoder is integrated with an attention
mechanism to adaptively recalibrate point-wise feature responses by explicitly
modeling interdependencies between different neuron units. We also introduce a
non-linear face reconstruction representation as a guidance of latent space to
obtain more accurate deformation, which helps solve the geometry-related
deformation and is good for generalization across subjects. Huber and HSIC
(Hilbert-Schmidt Independence Criterion) constraints are adopted to promote the
robustness of our model and to better exploit the non-linear and high-order
correlations. Experimental results on the public dataset and real scanned
dataset validate the superiority of our proposed GDPnet compared with
state-of-the-art model.

本文介绍一种名为几何引导密集透视网络（GDPnet）的深度学习架构，该架构具有高鲁棒性和能够实现说话者无关和非线性人脸动画，该方法在公共数据集和真实扫描数据集上都表现出优越性。