{"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/synthesizing-images-of-humans-in-unseen-poses","title":"Synthesizing Images of Humans in Unseen Poses","arxiv_id":"1804.07739","date":"2018-04-20","proceeding":"CVPR 2018 6","authors":["Guha Balakrishnan","Amy Zhao","Adrian V. Dalca","Fredo Durand","John Guttag"],"abstract":"We address the computational problem of novel human pose synthesis. Given an\nimage of a person and a desired pose, we produce a depiction of that person in\nthat pose, retaining the appearance of both the person and background. We\npresent a modular generative neural network that synthesizes unseen poses using\ntraining pairs of images and poses taken from human action videos. Our network\nseparates a scene into different body part and background layers, moves body\nparts to new locations and refines their appearances, and composites the new\nforeground with a hole-filled background. These subtasks, implemented with\nseparate modules, are trained jointly using only a single target image as a\nsupervised label. We use an adversarial discriminator to force our network to\nsynthesize realistic details conditioned on pose. We demonstrate image\nsynthesis results on three action classes: golf, yoga/workouts and tennis, and\nshow that our method produces accurate results within action classes as well as\nacross action classes. Given a sequence of desired poses, we also produce\ncoherent videos of actions.","url_abs":"http://arxiv.org/abs/1804.07739v1","url_pdf":"http://arxiv.org/pdf/1804.07739v1.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":"synthesizing-images-of-humans-in-unseen-poses","repo_url":"https://github.com/balakg/posewarp-cvpr2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.07739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.07739"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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