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

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 a novel alignment-before-generation approach to tackle the challenging task of
generating general 3D shapes based on 2D images or texts. Directly learning a conditional …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

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 …

Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a
reference person image. Existing methods usually build up multi-stage frameworks to deal …

While recent work has shown progress on extracting clothed 3D human avatars from a
single image, video, or a set of 3D scans, several limitations remain. Most methods use a …

High-velocity dynamic actions (eg, fling or throw) play a crucial role in our everyday
interaction with deformable objects by improving our efficiency and effectively expanding our …

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 …