High-quality 3D face asset creation remains costly due to reliance on controlled capture setups and manual processing, limiting scalability and diversity. We introduce a fully automated, semantically controllable framework for generating PBR-ready 3D facial assets without requiring dedicated scans. Our pipeline begins with a diffusion-based data synthesis stage, where 2D portrait samples from a pre-trained diffusion model are converted into 44K textured 3D face reconstructions via our proposed geometry recovery and texture normalization algorithm, which aligns arbitrarily shaded outputs into clean albedo space. Using this dataset, we train a disentangled adversarial generator that maps semantic attributes (age, gender, ethnicity) to UV-space geometry and albedo, enabling both direct sampling and continuous latent editing while preserving identity. A refinement stage further produces PBR materials and secondary assets (eyeballs, teeth, gums). The resulting system supports controllable face generation and post-editing in real time and exports directly to standard rendering and animation pipelines. We evaluate each component extensively and provide a web-based interactive interface to showcase practical deployment.

We present Re current F eature A lignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603 mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.