ExpDiff: Generating High-Fidelity 3D Facial Expression Meshes and BRDF Textures via Diffusion Model

3D face generation is a critical task for immersive multimedia applications, where a key challenge is the joint synthesis of expressive geometry and BRDF textures. Existing methods often struggle with geometric-textural coherence and corresponding reflectance modeling. To overcome these limitations, we present ExpDiff, a framework that generates expression meshes and corresponding BRDF textures from a single neutral-expression face. Our method employs an attention-based diffusion model to learn the semantic transition across expressions. To ensure correspondence between geometry and texture, we introduce a unified representation that explicitly models geometric-textural interaction, which is encoded into a shared latent space by models pre-trained on a vast dataset for strong generalization. To achieve semantically coherent and physically consistent generation, we propose to guide the denoising direction with specially designed textual prompts. We further construct two novel facial expression datasets, J-Reflectance, for ultra-high-quality assets, and FFHQ-BRDFExp for diverse identities, both of which are publicly released to advance the community. Extensive experiments demonstrate our method's superior performance in photo-realistic facial expression synthesis. Project page:https://cyh-sj.github.io/expdiff/.