We present GaussianAvatar an efficient approach to creating realistic human avatars with
dynamic 3D appearances from a single video. We start by introducing animatable 3D …

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 …

Representing human performance at high-fidelity is an essential building block in diverse
applications, such as film production, computer games or videoconferencing. To close the …

The ability to create realistic animatable and relightable head avatars from casual video
sequences would open up wide ranging applications in communication and entertainment …

Abstract We introduce Gaussian Articulated Template Model (GART) an explicit efficient and
expressive representation for non-rigid articulated subject capturing and rendering from …

Abstract We present Vid2Avatar, a method to learn human avatars from monocular in-the-
wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos …

We introduce an approach that creates animatable human avatars from monocular videos
using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields …

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 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 …

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 …