A Generalist FaceX via Learning Unified Facial Representation

• URL: http://arxiv.org/abs/2401.00551v1
• Date: Sun, 31 Dec 2023 17:41:48 GMT
• Title: A Generalist FaceX via Learning Unified Facial Representation
• Authors: Yue Han, Jiangning Zhang, Junwei Zhu, Xiangtai Li, Yanhao Ge, Wei Li, Chengjie Wang, Yong Liu, Xiaoming Liu, Ying Tai
• Abstract summary: FaceX is a novel facial generalist model capable of handling diverse facial tasks simultaneously. Our versatile FaceX achieves competitive performance compared to elaborate task-specific models on popular facial editing tasks.
• Score: 77.74407008931486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously. To achieve this goal, we initially formulate a unified facial representation for a broad spectrum of facial editing tasks, which macroscopically decomposes a face into fundamental identity, intra-personal variation, and environmental factors. Based on this, we introduce Facial Omni-Representation Decomposing (FORD) for seamless manipulation of various facial components, microscopically decomposing the core aspects of most facial editing tasks. Furthermore, by leveraging the prior of a pretrained StableDiffusion (SD) to enhance generation quality and accelerate training, we design Facial Omni-Representation Steering (FORS) to first assemble unified facial representations and then effectively steer the SD-aware generation process by the efficient Facial Representation Controller (FRC). %Without any additional features, Our versatile FaceX achieves competitive performance compared to elaborate task-specific models on popular facial editing tasks. Full codes and models will be available at https://github.com/diffusion-facex/FaceX.

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