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 …

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 …

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

Real-time rendering of photorealistic and controllable human avatars stands as a
cornerstone in Computer Vision and Graphics. While recent advances in neural implicit …

This work addresses the problem of real-time rendering of photorealistic human body
avatars learned from multi-view videos. While the classical approaches to model and render …

Gaussian splatting has emerged as a powerful 3D representation that harnesses the
advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper we …

We address the problem of learning person-specific facial priors from a small number (eg,
20) of portrait photos of the same person. This enables us to edit this specific person's facial …

We present PrimDiffusion, the first diffusion-based framework for 3D human generation.
Devising diffusion models for 3D human generation is difficult due to the intensive …

We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human
bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either …