Detailed Garment Recovery from a Single-View Image

TL;DR

This paper introduces a novel method for reconstructing detailed 3D garments from a single image, enabling applications like virtual try-on and cloth animation without multi-view data.

Contribution

It presents a new single-view garment recovery technique that combines priors, semantic parsing, and physics-based simulation for detailed 3D modeling.

Findings

Accurately captures global shape and small details of garments from one image.

Enables virtual try-on and garment transfer applications.

Demonstrates effective cloth animation for digital characters.

Abstract

Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, we pro- pose a method that is able to compute a rich and realistic 3D model of a human body and its outfits from a single photograph with little human in- teraction. Our algorithm is not only able to capture the global shape and geometry of the clothing, it can also extract small but important details of cloth, such as occluded wrinkles and folds. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or sampled 3D geom- etry), our approach achieves detailed garment recovery from a single-view image by using statistical, geometric, and physical priors and a combina- tion of parameter estimation, semantic parsing, shape recovery, and physics- based cloth simulation. We demonstrate the effectiveness of our algorithm by re-purposing the reconstructed garments for virtual try-on and garment transfer applications, as well as cloth animation for digital characters.