{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gp-gan-gender-preserving-gan-for-synthesizing","title":"GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks","arxiv_id":"1710.00962","date":"2017-10-03","proceeding":null,"authors":["Xing Di","Vishwanath A. Sindagi","Vishal M. Patel"],"abstract":"Facial landmarks constitute the most compressed representation of faces and\nare known to preserve information such as pose, gender and facial structure\npresent in the faces. Several works exist that attempt to perform high-level\nface-related analysis tasks based on landmarks. In contrast, in this work, an\nattempt is made to tackle the inverse problem of synthesizing faces from their\nrespective landmarks. The primary aim of this work is to demonstrate that\ninformation preserved by landmarks (gender in particular) can be further\naccentuated by leveraging generative models to synthesize corresponding faces.\nThough the problem is particularly challenging due to its ill-posed nature, we\nbelieve that successful synthesis will enable several applications such as\nboosting performance of high-level face related tasks using landmark points and\nperforming dataset augmentation. To this end, a novel face-synthesis method\nknown as Gender Preserving Generative Adversarial Network (GP-GAN) that is\nguided by adversarial loss, perceptual loss and a gender preserving loss is\npresented. Further, we propose a novel generator sub-network UDeNet for GP-GAN\nthat leverages advantages of U-Net and DenseNet architectures. Extensive\nexperiments and comparison with recent methods are performed to verify the\neffectiveness of the proposed method.","url_abs":"http://arxiv.org/abs/1710.00962v2","url_pdf":"http://arxiv.org/pdf/1710.00962v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gp-gan-gender-preserving-gan-for-synthesizing","repo_url":"https://github.com/DetionDX/GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"gp-gan-gender-preserving-gan-for-synthesizing","repo_url":"https://github.com/DetionDX/GP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"face-generation","task_name":"Face Generation"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}