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In this paper, a perceptual metrics guided GAN (PIGGAN) framework is proposed to intrinsically optimize generation processing for pose transfer task. Specifically, a novel and general model-Evaluator that matches well the GAN is designed. Accordingly, a new Sort Loss (SL) is constructed to optimize the perceptual quality. Morevover, PIGGAN is highly flexible and extensible and can incorporate both differentiable and indifferentiable indexes to optimize the attitude migration process. Extensive experiments show that PIGGAN can generate photo-realistic results and quantitatively outperforms state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.3233\/ica-210672","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T11:48:09Z","timestamp":1639482489000},"page":"141-151","source":"Crossref","is-referenced-by-count":40,"title":["Perceptual metric-guided human image generation"],"prefix":"10.1177","volume":"29","author":[{"given":"Haoran","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fazhi","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yansong","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohu","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/ICA-210672_ref1","doi-asserted-by":"crossref","unstructured":"Zhu Z, Huang T, Shi B, Yu M, Wang B, Bai X. 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