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Synthesizing high-quality 3D face models from natural language descriptionsis very valuable for many applications, including avatar creation, virtualreality, and telepresence. However, little research ever tapped into this task.We argue the major obstacle lies in 1) the lack of high-quality 3D face datawith descriptive text annotation, and 2) the complex mapping relationshipbetween descriptive language space and shape/appearance space. To solve theseproblems, we build Describe3D dataset, the first large-scale dataset withfine-grained text descriptions for text-to-3D face generation task. Then wepropose a two-stage framework to first generate a 3D face that matches theconcrete descriptions, then optimize the parameters in the 3D shape and texturespace with abstract description to refine the 3D face model. Extensiveexperimental results show that our method can produce a faithful 3D face thatconforms to the input descriptions with higher accuracy and quality thanprevious methods. The code and Describe3D dataset are released athttps://github.com/zhuhao-nju/describe3d .

High-Fidelity 3D Face Generation from Natural Language Descriptions