High-fidelity 3D Face Generation from Natural Language Descriptions

Hao Zhu, Menghua Wu, Xun Cao, Yuanxun Lu, Yiyu Zhuang, Linjia Huang

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Abstract

Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of highquality 3D face data with descriptive text annotation, and 2) the complex mapping relationship between descriptive language space and shape/appearance space. To solve these problems, we build DESCRIBE3D dataset, the first large-scale dataset with fine-grained text descriptions for text-to-3D face generation task. Then we propose a twostage framework to first generate a 3D face that matches the concrete descriptions, then optimize the parameters in the 3D shape and texture space with abstract description to refine the 3D face model. Extensive experimental results show that our method can produce a faithful 3D face that conforms to the input descriptions with higher accuracy and quality than previous methods. The code and DE-SCRIBE3D dataset are released at https://github. com/zhuhao-nju/describe3d.

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