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

We present FITE, a First-Implicit-Then-Explicit framework for modeling human avatars in
clothing. Our framework first learns implicit surface templates representing the coarse …

Learning to model and reconstruct humans in clothing is challenging due to articulation, non-
rigid deformation, and varying clothing types and topologies. To enable learning, the choice …

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 …

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 …

Parametric 3D body models like SMPL only represent minimally-clothed people and are
hard to extend to clothing because they have a fixed mesh topology and resolution. To …

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 …

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 …

We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for
rigged garments through deep learning. Classical approaches rely on Physically Based …