GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images

• URL: http://arxiv.org/abs/2308.03413v1
• Date: Mon, 7 Aug 2023 09:03:35 GMT
• Title: GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images
• Authors: Tianxiang Ma, Bingchuan Li, Qian He, Jing Dong, Tieniu Tan
• Abstract summary: We introduce a novel Geometry-aware Facial Expression Translation framework, which is based on parametric 3D facial representations and can stably decoupled expression. We achieve higher-quality and more accurate facial expression transfer results compared to state-of-the-art methods, and demonstrate applicability of various poses and complex textures.
• Score: 55.431697263581626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While current face animation methods can manipulate expressions individually, they suffer from several limitations. The expressions manipulated by some motion-based facial reenactment models are crude. Other ideas modeled with facial action units cannot generalize to arbitrary expressions not covered by annotations. In this paper, we introduce a novel Geometry-aware Facial Expression Translation (GaFET) framework, which is based on parametric 3D facial representations and can stably decoupled expression. Among them, a Multi-level Feature Aligned Transformer is proposed to complement non-geometric facial detail features while addressing the alignment challenge of spatial features. Further, we design a De-expression model based on StyleGAN, in order to reduce the learning difficulty of GaFET in unpaired "in-the-wild" images. Extensive qualitative and quantitative experiments demonstrate that we achieve higher-quality and more accurate facial expression transfer results compared to state-of-the-art methods, and demonstrate applicability of various poses and complex textures. Besides, videos or annotated training data are omitted, making our method easier to use and generalize.

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