The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is
enabling the creation of detailed, clothed, 3D humans from images. However, existing …

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 …

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

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 …

Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only
collections of single-view 2D photographs has very recently made much progress. These 3D …

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

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

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 …