The realm of textiles spans clothing, households, healthcare, sports, and industrial
applications. The deformable nature of these objects poses unique challenges that prior …

Fashion is the way we present ourselves to the world and has become one of the world's
largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention …

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 …

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 …

In the past, data in which science and engineering is based, was scarce and frequently
obtained by experiments proposed to verify a given hypothesis. Each experiment was able …

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 propose a method that leverages graph neural networks, multi-level message passing,
and unsupervised training to enable real-time prediction of realistic clothing dynamics …

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 …

We present CaPhy, a novel method for reconstructing animatable human avatars with
realistic dynamic properties for clothing. Specifically, we aim for capturing the geometric and …

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 …