  Generating photorealistic 3D faces from given conditions is a challenging
task. Existing methods often rely on time-consuming one-by-one optimization
approaches, which are not efficient for modeling the same distribution content,
e.g., faces. Additionally, an ideal controllable 3D face generation model
should consider both facial attributes and expressions. Thus we propose a novel
approach called TEx-Face(TExt & Expression-to-Face) that addresses these
challenges by dividing the task into three components, i.e., 3D GAN Inversion,
Conditional Style Code Diffusion, and 3D Face Decoding. For 3D GAN inversion,
we introduce two methods which aim to enhance the representation of style codes
and alleviate 3D inconsistencies. Furthermore, we design a style code denoiser
to incorporate multiple conditions into the style code and propose a data
augmentation strategy to address the issue of insufficient paired
visual-language data. Extensive experiments conducted on FFHQ, CelebA-HQ, and
CelebA-Dialog demonstrate the promising performance of our TEx-Face in
achieving the efficient and controllable generation of photorealistic 3D faces.
The code will be available at https://github.com/sxl142/TEx-Face.
