TY - GEN
T1 - Unsupervised disentangling of appearance and geometry by deformable generator network
AU - Xing, Xianglei
AU - Han, Tian
AU - Gao, Ruiqi
AU - Zhu, Song Chun
AU - Wu, Ying Nian
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - 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 coordinates of each pixel to obtain the final image. Two generators act upon independent latent factors to extract disentangled appearance and geometric information from image. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments show that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to the other image datasets to facilitate knowledge transfer tasks.
AB - 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 coordinates of each pixel to obtain the final image. Two generators act upon independent latent factors to extract disentangled appearance and geometric information from image. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments show that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to the other image datasets to facilitate knowledge transfer tasks.
KW - And Body Pose
KW - Deep Learning
KW - Face
KW - Gesture
KW - Image and Video Synthesis
KW - Representation Learning
KW - Statistical Learning
UR - http://www.scopus.com/inward/record.url?scp=85074769082&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074769082&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2019.01060
DO - 10.1109/CVPR.2019.01060
M3 - Conference contribution
AN - SCOPUS:85074769082
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 10346
EP - 10355
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Y2 - 16 June 2019 through 20 June 2019
ER -