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Pose-Guided Photorealistic Face Rotation
Yibo Hu1,2; Xiang Wu1; Bing Yu3; Ran He(赫然)1,2; Zhenan Sun1,2
Conference NameIEEE Computer Vision and Pattern Recognition
Conference Date2018
Conference PlaceSalt Lake City, USA
AbstractFace rotation provides an effective and cheap way for data augmentation and representation learning of face recognition. It is a challenging generative learning problem due to the large pose discrepancy between two face images. This work focuses on flexible face rotation of arbitrary head poses, including extreme profile views. We propose a novel Couple-Agent Pose-Guided Generative Adversarial Network (CAPG-GAN) to generate both neutral and profile head pose face images. The head pose information is encoded by facial landmark heatmaps. It not only forms a mask image to guide the generator in learning process but also provides a flexible controllable condition during inference. A couple-agent discriminator is introduced to reinforce on the realism of synthetic arbitrary view faces. Besides the generator and conditional adversarial loss, CAPG-GAN further employs identity preserving loss and total variation regularization to preserve identity information and refine local textures respectively. Quantitative and qualitative experimental results on the Multi-PIE and LFW databases consistently show the superiority of our face rotation method over the state-of-the-art.
KeywordFace Rotation Couple-agent Pose-guided Generative Adversarial Network Generative Learning
Indexed ByEI
Document Type会议论文
Affiliation1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Noah’s Ark Laboratory, Huawei Technologies Co., Ltd.
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yibo Hu,Xiang Wu,Bing Yu,et al. Pose-Guided Photorealistic Face Rotation[C],2018.
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