We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

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 …

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 …

We present a simple yet effective method to infer detailed full human body shape from only a
single photograph. Our model can infer full-body shape including face, hair, and clothing …

This paper describes how to obtain accurate 3D body models and texture of arbitrary people
from a single, monocular video in which a person is moving. Based on a parametric body …

Designing and simulating realistic clothing is challenging. Previous methods addressing the
capture of clothing from 3D scans have been limited to single garments and simple motions …

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 …

We present a novel method to generate accurate and realistic clothing deformation from real
data capture. Previous methods for realistic cloth modeling mainly rely on intensive …

The fashion industry is on the verge of an unprecedented change. The implementation of
machine learning, computer vision, and artificial intelligence (AI) in fashion applications is …

The rapid progress of computer vision, machine learning, and artificial intelligence
combined with the current growing urge for online shopping systems opened an excellent …