We introduce a free-viewpoint rendering method--HumanNeRF--that works on a given
monocular video of a human performing complex body motions, eg a video from YouTube …

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

Modeling animatable human avatars from RGB videos is a long-standing and challenging
problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent …

Current methods for learning realistic and animatable 3D clothed avatars need either posed
3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn …

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

We present GauHuman a 3D human model with Gaussian Splatting for both fast training (1 2
minutes) and real-time rendering (up to 189 FPS) compared with existing NeRF-based …

Recent neural human representations can produce high-quality multi-view rendering but
require using dense multi-view inputs and costly training. They are hence largely limited to …

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video
data. The learnt avatar not only provides expressive control of the body, hands and the face …

In this paper, we take one step further towards real-world applicability of monocular neural
avatar reconstruction by contributing InstantAvatar, a system that can reconstruct human …

We present Pose-NDF, a continuous model for plausible human poses based on neural
distance fields (NDFs). Pose or motion priors are important for generating realistic new …