This paper presents a 3D diffusion model that automatically generates 3D digital avatars
represented as neural radiance fields (NeRFs). A significant challenge for 3D diffusion is …

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 …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Purely MLP-based neural radiance fields (NeRF-based methods) often suffer from
underfitting with blurred renderings on large-scale scenes due to limited model capacity …

Traditional 3D morphable face models (3DMMs) provide fine-grained control over
expression but cannot easily capture geometric and appearance details. Neural volumetric …

Abstract We present Neural Head Avatars, a novel neural representation that explicitly
models the surface geometry and appearance of an animatable human avatar that can be …

As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-
quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains …

We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for
photorealistic 3D human reconstruction given just a monocular RGB image. Our pixel …

This paper addresses the challenge of efficiently reconstructing volumetric videos of
dynamic humans from sparse multi-view videos. Some recent works represent a dynamic …

We present X-Avatar, a novel avatar model that captures the full expressiveness of digital
humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our …