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Generating 3D faces from textual descriptions has a multitude ofapplications, such as gaming, movie, and robotics. Recent progresses havedemonstrated the success of unconditional 3D face generation and text-to-3Dshape generation. However, due to the limited text-3D face data pairs,text-driven 3D face generation remains an open problem. In this paper, wepropose a text-guided 3D faces generation method, refer as TG-3DFace, forgenerating realistic 3D faces using text guidance. Specifically, we adopt anunconditional 3D face generation framework and equip it with text conditions,which learns the text-guided 3D face generation with only text-2D face data. Ontop of that, we propose two text-to-face cross-modal alignment techniques,including the global contrastive learning and the fine-grained alignmentmodule, to facilitate high semantic consistency between generated 3D faces andinput texts. Besides, we present directional classifier guidance during theinference process, which encourages creativity for out-of-domain generations.Compared to the existing methods, TG-3DFace creates more realistic andaesthetically pleasing 3D faces, boosting 9% multi-view consistency (MVIC) overLatent3D. The rendered face images generated by TG-3DFace achieve higher FIDand CLIP score than text-to-2D face/image generation models, demonstrating oursuperiority in generating realistic and semantic-consistent textures.

Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images