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Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented\n            <jats:bold>D<\/jats:bold>\n            isentangled\n            <jats:bold>F<\/jats:bold>\n            eature\n            <jats:bold>C<\/jats:bold>\n            onsistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on\n            <jats:sc>Mixamo-Pose<\/jats:sc>\n            and\n            <jats:sc>EDN-10k<\/jats:sc>\n            have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.\n          <\/jats:p>","DOI":"10.1145\/3626241","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T15:27:13Z","timestamp":1697210833000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Human Pose Transfer with Augmented Disentangled Feature Consistency"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2095-2140","authenticated-orcid":false,"given":"Kun","family":"Wu","sequence":"first","affiliation":[{"name":"Syracuse University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3238-960X","authenticated-orcid":false,"given":"Chengxiang","family":"Yin","sequence":"additional","affiliation":[{"name":"Syracuse University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6818-1125","authenticated-orcid":false,"given":"Zhengping","family":"Che","sequence":"additional","affiliation":[{"name":"Midea Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4175-6029","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Didi Chuxing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4418-0114","authenticated-orcid":false,"given":"Jian","family":"Tang","sequence":"additional","affiliation":[{"name":"Midea Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5635-8883","authenticated-orcid":false,"given":"Zheng","family":"Guan","sequence":"additional","affiliation":[{"name":"Computer Science School, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6241-1613","authenticated-orcid":false,"given":"Gangyi","family":"Ding","sequence":"additional","affiliation":[{"name":"Computer Science School, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322999"},{"key":"e_1_3_1_3_2","unstructured":"Adobe Systems Inc.2018. 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