TY - GEN
T1 - Interactive Pose Attention Network for Human Pose Transfer
AU - Luo, Di
AU - Zhang, Guipeng
AU - Yang, Zhenguo
AU - Yuan, Minzheng
AU - Tao, Tao
AU - Xu, Liangliang
AU - Li, Qing
AU - Liu, Wenyin
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2022/1
Y1 - 2022/1
N2 - In this paper, we propose an end-to-end interactive pose attention network (IPAN) to generate the person image in a target pose, where the generator of the network comprises a sequence of interactive pose attention (IPA) blocks to transfer the attended regions regarding to intermedia poses progressively, and retain the texture details of the unattended regions for subsequent pose transfer. More specifically, we design an attention mechanism by interacting with image and pose pathways to transfer the regions of interest based on the human pose, and capture the uninterested regions in the current IPA block against the uncertainty of the intermedia poses. In particular, we devise long-distance residual to inject the low-level features of the person image into the IPA blocks to keep its appearance characteristics. In terms of adversarial training, the generator exploits reconstruction loss, perceptual loss and contextual loss, and the discriminator exploits the adversarial loss. Quantitative and qualitative experiments conducted on the DeepFashion and Market-1501 datasets demonstrate the superior performance of the proposed method (e.g., FID value is reduced from 36.708 to 22.568 and 15.757 to 12.835 on Market-1501 and DeepFashion datasets, respectively).
AB - In this paper, we propose an end-to-end interactive pose attention network (IPAN) to generate the person image in a target pose, where the generator of the network comprises a sequence of interactive pose attention (IPA) blocks to transfer the attended regions regarding to intermedia poses progressively, and retain the texture details of the unattended regions for subsequent pose transfer. More specifically, we design an attention mechanism by interacting with image and pose pathways to transfer the regions of interest based on the human pose, and capture the uninterested regions in the current IPA block against the uncertainty of the intermedia poses. In particular, we devise long-distance residual to inject the low-level features of the person image into the IPA blocks to keep its appearance characteristics. In terms of adversarial training, the generator exploits reconstruction loss, perceptual loss and contextual loss, and the discriminator exploits the adversarial loss. Quantitative and qualitative experiments conducted on the DeepFashion and Market-1501 datasets demonstrate the superior performance of the proposed method (e.g., FID value is reduced from 36.708 to 22.568 and 15.757 to 12.835 on Market-1501 and DeepFashion datasets, respectively).
KW - Human pose transfer
KW - Interactive pose attention
KW - Long-distance residual
UR - http://www.scopus.com/inward/record.url?scp=85121924613&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-91560-5_2
DO - 10.1007/978-3-030-91560-5_2
M3 - Conference article published in proceeding or book
AN - SCOPUS:85121924613
SN - 9783030915599
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 18
EP - 33
BT - Web Information Systems Engineering - WISE 2021 - 22nd International Conference on Web Information Systems Engineering, WISE 2021, Proceedings
A2 - Zhang, Wenjie
A2 - Zou, Lei
A2 - Maamar, Zakaria
A2 - Chen, Lu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Web Information Systems Engineering, WISE 2021
Y2 - 26 October 2021 through 29 October 2021
ER -