{"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/learning-from-synthetic-humans","title":"Learning from Synthetic Humans","arxiv_id":"1701.01370","date":"2017-01-05","proceeding":"CVPR 2017 7","authors":["Gül Varol","Javier Romero","Xavier Martin","Naureen Mahmood","Michael J. Black","Ivan Laptev","Cordelia Schmid"],"abstract":"Estimating human pose, shape, and motion from images and videos are\nfundamental challenges with many applications. Recent advances in 2D human pose\nestimation use large amounts of manually-labeled training data for learning\nconvolutional neural networks (CNNs). Such data is time consuming to acquire\nand difficult to extend. Moreover, manual labeling of 3D pose, depth and motion\nis impractical. In this work we present SURREAL (Synthetic hUmans foR REAL\ntasks): a new large-scale dataset with synthetically-generated but realistic\nimages of people rendered from 3D sequences of human motion capture data. We\ngenerate more than 6 million frames together with ground truth pose, depth\nmaps, and segmentation masks. We show that CNNs trained on our synthetic\ndataset allow for accurate human depth estimation and human part segmentation\nin real RGB images. Our results and the new dataset open up new possibilities\nfor advancing person analysis using cheap and large-scale synthetic data.","url_abs":"http://arxiv.org/abs/1701.01370v3","url_pdf":"http://arxiv.org/pdf/1701.01370v3.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":"learning-from-synthetic-humans","repo_url":"https://github.com/chingswy/HumanPoseMemo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"learning-from-synthetic-humans","repo_url":"https://github.com/gulvarol/surreal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"2d-human-pose-estimation","task_name":"2D Human Pose Estimation"},{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"human-part-segmentation","task_name":"Human Part Segmentation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1701.01370","atlas_url":"https://app.syntology.ai/?focus=1701.01370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.01370"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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