Pose transfer based on generative adversarial networks

Hao Pan; Xincong Cao

doi:10.1117/12.2578040

10 November 2020 Pose transfer based on generative adversarial networks

Hao Pan, Xincong Cao

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158412 (2020) https://doi.org/10.1117/12.2578040
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

The person-related vision tasks face many challenges, such as insufficient or lack of diversity of datasets and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. To address this issue, this paper proposes a human posture transfer method based on improving CycleGAN. Transferring a given person’s posture to the target one, while keep the character's identity unchanged, thereby augment the diversity of datasets. The generator of the network contains a sequence of transfer blocks which have similar structures, each transfer block is utilized to transform different part of body as a local transfer. Therefore, avoiding learning the complex structure of the global manifold and address the large spatial misalignment issues induced by transformations of target pose. The discriminator of the network comprised of two convolutional neural networks, to judge the appearance and shape. Quantitative comparisons with state-of-the-art, the proposed method can generate images with the highest score on the metrics, and get performance boost of Re-ID on account of data augmentation.

Citation Download Citation

Hao Pan and Xincong Cao "Pose transfer based on generative adversarial networks", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158412 (10 November 2020); https://doi.org/10.1117/12.2578040

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