Unsupervised Learning of Style-Aware Facial Animation from Real Acting Performances

• URL: http://arxiv.org/abs/2306.10006v3
• Date: Fri, 1 Sep 2023 18:08:05 GMT
• Title: Unsupervised Learning of Style-Aware Facial Animation from Real Acting Performances
• Authors: Wolfgang Paier and Anna Hilsmann and Peter Eisert
• Abstract summary: We present a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Our animation method is based on a conditional CNN that transforms text or speech into a sequence of animation parameters. For realistic real-time rendering, we train a U-Net that refines pixelization-based renderings by computing improved colors and a foreground matte.
• Score: 3.95944314850151
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for accurate capturing and realistic synthesis of facial expressions from a latent feature vector. Our animation method is based on a conditional CNN that transforms text or speech into a sequence of animation parameters. In contrast to previous approaches, our animation model learns disentangling/synthesizing different acting-styles in an unsupervised manner, requiring only phonetic labels that describe the content of training sequences. For realistic real-time rendering, we train a U-Net that refines rasterization-based renderings by computing improved pixel colors and a foreground matte. We compare our framework qualitatively/quantitatively against recent methods for head modeling as well as facial animation and evaluate the perceived rendering/animation quality in a user-study, which indicates large improvements compared to state-of-the-art approaches

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