DiffFAE: Advancing High-fidelity One-shot Facial Appearance Editing with Space-sensitive Customization and Semantic Preservation

no code implementations • 26 Mar 2024 • Qilin Wang, Jiangning Zhang, Chengming Xu, Weijian Cao, Ying Tai, Yue Han, Yanhao Ge, Hong Gu, Chengjie Wang, Yanwei Fu

Facial Appearance Editing (FAE) aims to modify physical attributes, such as pose, expression and lighting, of human facial images while preserving attributes like identity and background, showing great importance in photograph.

Attribute Semantic Composition

TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation

no code implementations • 19 Mar 2024 • Yufei Liu, Junwei Zhu, Junshu Tang, Shijie Zhang, Jiangning Zhang, Weijian Cao, Chengjie Wang, Yunsheng Wu, Dongjin Huang

Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV.

Text to 3D Texture Synthesis

Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild

no code implementations • CVPR 2023 • Zhenyu Zhang, Renwang Chen, Weijian Cao, Ying Tai, Chengjie Wang

To address this problem, this paper presents a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling.

Physically-Guided Disentangled Implicit Rendering for 3D Face Modeling

no code implementations • CVPR 2022 • Zhenyu Zhang, Yanhao Ge, Ying Tai, Weijian Cao, Renwang Chen, Kunlin Liu, Hao Tang, Xiaoming Huang, Chengjie Wang, Zhifeng Xie, Dongjin Huang

This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for high-fidelity 3D face modeling.

3D Face Modelling Neural Rendering