PSO-HEAD: Pseudo-Supervision Guided Spatial Optimization for View-consistent 3D Full-Head Reconstruction

Generative 3D human head reconstruction in \(360∘\) is attracting increasing attention because of its flexibility in downstream animation applications. Existing generative 3D head synthesis approaches are primarily limited to near-frontal face priors, which cause distorted artifacts at large view angles. In this paper, we introduce a novel Pseudo-Supervision guided Spatial Optimization (PSO-HEAD) framework that reconstructs 3D view-consistent full-head through explicitly introducing pseudo-label of back-head supervision for spatial texture and geometric optimization. Particularly, our PSO-HEAD introduces two key improvements, i.e. , Pseudo-Supervision Augmented Inversion (PSA-Inversion) and Full-head Aware Generative Enhancement (FAGE). PSA-Inversion augments plausible invisible back-head as pseudo supervision to optimize the view-hallucinated latent code conditioned on the augmented camera poses via GAN inversion, enforcing 3D spatial consistency across both visible and invisible regions. Furthermore, FAGE fine-tunes the 3D GAN on a proposed auxiliary FK-Enhance dataset deriving from either generated or real-world high-quality back-head images, which therefore improves the generalization of our PSO-HEAD to diverse hairstyles or underrepresented regions. Benefiting from the improvements, our PSO-HEAD enables efficient \(360∘\) view-consistent full-head generation from single input images, particularly improving reconstruction fidelity of unobserved regions, which quantitatively and qualitatively outperforms the state-of-the-art methods.