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

Geometry-guided Dense Perspective Network for Speech-Driven Facial Animation