Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great
potential in novel view synthesis of a scene. However, current NeRF-based methods cannot …

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a
clothed human and turns them into an animatable avatar. These avatars are driven by pose …

Neural implicit surface representations have emerged as a promising paradigm to capture
3D shapes in a continuous and resolution-independent manner. However, adapting them to …

The SMPL body model is widely used for the estimation, synthesis, and analysis of 3D
human pose and shape. While popular, we show that SMPL has several limitations and …

Abstract We present Neural Generalized Implicit Functions (Neural-GIF), to animate people
in clothing as a function of the body pose. Given a sequence of scans of a subject in various …

Currently it requires an artist to create 3D human avatars with realistic clothing that can
move naturally. Despite progress on 3D scanning and modeling of human bodies, there is …

In this paper, we consider the problem to automatically reconstruct garment and body
shapes from a single near-front view RGB image. To this end, we propose a layered …

We propose a new generative model for 3D garment deformations that enables us to learn,
for first time, a data-driven method for virtual try-on that effectively addresses garment-body …

We present RigNet, an end-to-end automated method for producing animation rigs from
input character models. Given an input 3D model representing an articulated character …

Creating animatable avatars from static scans requires the modeling of clothing
deformations in different poses. Existing learning-based methods typically add pose …