Paper Info
Title
Unsupervised Disentangling Of Appearance And Geometry By Deformable Generator Network
Abstract
We present a deformable generator model to disentangle the appearance and geometric information in purely unsupervised manner. The appearance generator models the appearance related information, including color, illumination, identity or category, of an image, while the geometric generator performs geometric related warping, such as rotation and stretching, through generating displacement of the coordinate of each pixel to obtain the final image. Two generators act upon independent latent factors to extract disentangled appearance and geometric information from images. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments shows that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to other image datasets to facilitate knowledge transfer tasks.
Year
DOI
Venue
2019
10.1109/CVPR.2019.01060
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
ISSN
Computer vision,Pattern recognition,Computer science,Artificial intelligence
Conference
1063-6919
Citations 
PageRank 
References 
2
0.36
0
Authors
5
Name
Order
Citations
PageRank
Xianglei Xing19610.51
Tian Han2236.21
Ruiqi Gao3219.35
Song-Chun Zhu46580741.75
Ying Nian Wu51652267.72