Log In Sign Up

Towards 3D Human Shape Recovery Under Clothing

by   Xin Chen, et al.

We present a learning-based scheme for robustly and accurately estimating clothing fitness as well as the human shape on clothed 3D human scans. Our approach maps the clothed human geometry to a geometry image that we call clothed-GI. To align clothed-GI under different clothing, we extend the parametric human model and employ skeleton detection and warping for reliable alignment. For each pixel on the clothed-GI, we extract a feature vector including color/texture, position, normal, etc. and train a modified conditional GAN network for per-pixel fitness prediction using a comprehensive 3D clothing. Our technique significantly improves the accuracy of human shape prediction, especially under loose and fitted clothing. We further demonstrate using our results for human/clothing segmentation and virtual clothes fitting at a high visual realism.


page 3

page 8


Estimation of Human Body Shape and Posture Under Clothing

Estimating the body shape and posture of a dressed human subject in moti...

Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions using Similarity Domains Network

We demonstrate the use of similarity domains (SDs) for shape modeling an...

Tex2Shape: Detailed Full Human Body Geometry from a Single Image

We present a simple yet effective method to infer detailed full human bo...

SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator

Recent years have seen growing interest in 3D human faces modelling due ...

Significance of Skeleton-based Features in Virtual Try-On

The idea of Virtual Try-ON (VTON) benefits e-retailing by giving an user...

Automatic Image Pixel Clustering based on Mussels Wandering Optimiz

Image segmentation as a clustering problem is to identify pixel groups o...

PhysXNet: A Customizable Approach for LearningCloth Dynamics on Dressed People

We introduce PhysXNet, a learning-based approach to predict the dynamics...