TY - GEN
T1 - Spectral normalization and relativistic adversarial training for conditional pose generation with self-attention
AU - Horiuchi, Yusuke
AU - Iizuka, Satoshi
AU - Simo-Serra, Edgar
AU - Ishikawa, Hiroshi
N1 - Funding Information:
This work was partially supported by JST ACT-I (Iizuka, Grant Number: JPMJPR16U3), JST PRESTO (Simo-Serra, Grant Number: JPMJPR1756), and JST CREST (Ishikawa, Iizuka, and Simo-Serra, Grant Number: JPMJCR14D1).
Publisher Copyright:
© 2019 MVA Organization.
PY - 2019/5
Y1 - 2019/5
N2 - We address the problem of conditional image generation of synthesizing a new image of an individual given a reference image and target pose. We base our approach on generative adversarial networks and leverage deformable skip connections to deal with pixel-to-pixel misalignments, self-attention to leverage complementary features in separate portions of the image, e.g., arms or legs, and spectral normalization to improve the quality of the synthesized images. We train the synthesis model with a nearest-neighbour loss in combination with a relativistic average hinge adversarial loss. We evaluate on the Market-1501 dataset and show how our proposed approach can surpass existing approaches in conditional image synthesis performance.
AB - We address the problem of conditional image generation of synthesizing a new image of an individual given a reference image and target pose. We base our approach on generative adversarial networks and leverage deformable skip connections to deal with pixel-to-pixel misalignments, self-attention to leverage complementary features in separate portions of the image, e.g., arms or legs, and spectral normalization to improve the quality of the synthesized images. We train the synthesis model with a nearest-neighbour loss in combination with a relativistic average hinge adversarial loss. We evaluate on the Market-1501 dataset and show how our proposed approach can surpass existing approaches in conditional image synthesis performance.
UR - http://www.scopus.com/inward/record.url?scp=85070455014&partnerID=8YFLogxK
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U2 - 10.23919/MVA.2019.8758013
DO - 10.23919/MVA.2019.8758013
M3 - Conference contribution
AN - SCOPUS:85070455014
T3 - Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
BT - Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Machine Vision Applications, MVA 2019
Y2 - 27 May 2019 through 31 May 2019
ER -