Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation

Understand

Generative Neural Radiance Fields (GNeRF)-based 3D-aware GANs have showcased remarkable prowess in crafting high-fidelity images while upholding robust 3D consistency, particularly face generation.

• However, specific existing models prioritize view consistency over disentanglement, leading to constrained semantic or attribute control during the generation process.
• While many methods have explored incorporating semantic masks or leveraging 3D Morphable Models (3DMM) priors to imbue models with semantic control, these methods often demand training from scratch, entailing significant computational overhead.
• In this paper, we propose a novel approach: a conditional GNeRF model that integrates specific attribute labels as input, thus amplifying the controllability and disentanglement capabilities of 3D-aware generative models.