The fidelity of relighting is bounded by both geometry and appearance representations. For
geometry both mesh and volumetric approaches have difficulty modeling intricate structures …

We present a one-shot method to infer and render a photorealistic 3D representation from a
single unposed image (eg, face portrait) in real-time. Given a single RGB input, our image …

This paper tackles the problem of creating relightable and animatable neural avatars from
sparse-view (or monocular) videos of dynamic humans under unknown illumination …

Abstract We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic
3D head reconstructions from monocular RGB videos. To this end we propose a latent …

This paper presents a novel neural implicit radiance representation for free viewpoint
relighting from a small set of unstructured photographs of an object lit by a moving point light …

Existing one-shot 4D head synthesis methods usually learn from monocular videos with the
aid of 3DMM reconstruction yet the latter is evenly challenging which restricts them from …

Existing photorealistic relightable hand models require extensive identity-specific
observations in different views poses and illuminations and face challenges in generalizing …

We present the first neural relighting approach for rendering high-fidelity personalized
hands that can be animated in real-time under novel illumination. Our approach adopts a …

Eyeglasses play an important role in the perception of identity. Authentic virtual
representations of faces can benefit greatly from their inclusion. However, modeling the …

We propose RANA, a relightable and articulated neural avatar for the photorealistic
synthesis of humans under arbitrary viewpoints, body poses, and lighting. We only require a …