Faces in 3D, Guided by Geometry and Preferences
Today’s digest explores two ways to improve AI-generated faces in 3D: geometry-constrained multi-view diffusion for consistent head generation, and human-preference fine-tuning that sculpts face GAN geometry without explicit surface supervision.
GeoFace Teaser: Multi-view face image generation with mesh overlay showing geometric consistency. From GeoFace.
Digital Humans & 3D Faces
GeoFace
GeoFace: Consistent Multi-View Face Generation with Geometry-Constrained Diffusion
GeoFace generates multi-view face images with consistent 3D geometry from a single input using a dual-stream diffusion model. Its unique geometry-guided attention ensures all views share a photorealistic, aligned underlying 3D face structure, surpassing prior methods without explicit 3D constraints.
Sculpting NeRF Geometry
Sculpting NeRF Geometry: Human-Preference Fine-Tuning of a 3D-Aware Face GAN
This paper fine-tunes a pretrained 3D-aware face GAN's geometry using a reward model trained on human preferences directly from the radiance field's density values. It uniquely improves 3D facial geometry without relying on text prompts, mesh priors, or explicit surface supervision, while preserving 2D appearance.