Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

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 …

In this paper we introduce SMPLicit, a novel generative model to jointly represent body
pose, shape and clothing geometry. In contrast to existing learning-based approaches that …

Abstract We present Multi-Garment Network (MGN), a method to predict body shape and
clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several …

In this paper, we present TailorNet, a neural model which predicts clothing deformation in
3D as a function of three factors: pose, shape and style (garment geometry), while retaining …

Three-dimensional human body models are widely used in the analysis of human pose and
motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do …

We propose a deep videorealistic 3D human character model displaying highly realistic
shape, motion, and dynamic appearance learned in a new weakly supervised way from …

In this work we target a learnable output representation that allows continuous, high
resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a …

Modeling and rendering photorealistic avatars is of crucial importance in many applications.
Existing methods that build a 3D avatar from visual observations, however, struggle to …

We present a self-supervised method to learn dynamic 3D deformations of garments worn
by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment …