@article{ref1,
title="Pose and color-gamut guided generative adversarial network for pedestrian image synthesis",
journal="IEEE transactions on neural networks and learning systems",
year="2022",
author="Liu, Xiaokai and Liu, Xiang and Li, Gang and Bi, Sheng",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Tremendous transfer requirements in pedestrian reidentification (Re-ID) tasks have greatly promoted the remarkable success in pedestrian image synthesis, to relieve the inconsistency in poses and lighting. However, existing approaches are confined to transferring in a particular domain and are difficult to combine, since pose and color variables locate in two independent domains. To facilitate the research toward conquering this issue, we propose a pose and color-gamut guided generative adversarial network (PC-GAN) that performs joint-domain pedestrian image synthesis conditioned on certain pose and color-gamut through a delicate supervision design. The generator of the network comprises a sequence of cross-domain conversion subnets, where the local displacement estimator, color-gamut transformer, and pose transporter coordinate their learning pace to progressively synthesize images in desired pose and color-gamut. Ablation studies have demonstrated the efficacy and efficiency of the proposed network both qualitatively and quantitatively on Market-1501 and DukeMTMC. Furthermore, the proposed architecture can generate training images for person Re-ID, alleviating the data insufficiency problem.<p /> <p>Language: en</p>",
language="en",
issn="2162-237X",
doi="10.1109/TNNLS.2022.3171245",
url="http://dx.doi.org/10.1109/TNNLS.2022.3171245"
}