Paper Info
Title
Seeing through the appearance: Body shape estimation using multi-view clothing images
Abstract
We propose a learning-based algorithm for body shape estimation, which only requires 2D clothing images taken in multiple views as the input data. Compared with the use of 3D scanners or depth cameras, although our setting is more user friendly, it also makes the learning and estimation problems more challenging. In addition to utilizing ground truth body images for constructing human body models at each view of interest, our work uniquely associates the anthropometric measurements (e.g., body height or leg length) across different views. For performing body shape estimation using multi-view clothing images, the proposed algorithm solves an optimization task which recovers the body shape with image and measurement reconstruction guarantees. In the experiments, we will show that the use of our proposed method would achieve satisfactory estimation results, and performs favorably against single-view or other baseline approaches for both body shape and measurement estimation.
Year
DOI
Venue
2015
10.1109/ICME.2015.7177402
2015 IEEE International Conference on Multimedia and Expo (ICME)
Keywords
Field
DocType
Body shape estimation,multi-view image reconstruction,regression models
Computer vision,Pattern recognition,Computer science,Clothing,Body images,Ground truth,Artificial intelligence,User Friendly
Conference
ISSN
Citations 
PageRank 
1945-7871
0
0.34
References 
Authors
16
2
Name
Order
Citations
PageRank
Wei-Yi Chang141.08
Yu-Chiang Frank Wang291461.63