Facial Expression Parameters Extraction Using Graph Convolution Networks

This paper addresses a deep learning framework for the extraction of Facial Action Coding System coefficients from 3D facial models. To optimize the labor-intensive process associated with facial animation using traditional Blendshapes, this framework employs a Graph Convolution Network to extract feature vectors from 3D facial models, and accurately infers expression coefficients based on the Facial Action Coding System.