Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

Creating high-fidelity 3D head avatars has always been a research hotspot but there
remains a great challenge under lightweight sparse view setups. In this paper we propose …

We focus on reconstructing high-fidelity radiance fields of human heads, capturing their
animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time …

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 …

The problem of modeling an animatable 3D human head avatar under lightweight setups is
of significant importance but has not been well solved. Existing 3D representations either …

We propose a novel 3D morphable model for complete human heads based on hybrid
neural fields. At the core of our model lies a neural parametric representation that …

Existing approaches to animatable NeRF-based head avatars are either built upon face
templates or use the expression coefficients of templates as the driving signal. Despite the …

Abstract 3D head animation has seen major quality and runtime improvements over the last
few years, particularly empowered by the advances in differentiable rendering and neural …

In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering
optimization pipeline, that can be used to acquire high-fidelity renderable human avatars …

Recent work on radiance fields and volumetric inverse rendering (eg, NeRFs) has provided
excellent results in building data-driven models of real scenes for novel view synthesis with …