{"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/deforming-autoencoders-unsupervised","title":"Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance","arxiv_id":"1806.06503","date":"2018-06-18","proceeding":"ECCV 2018 9","authors":["Zhixin Shu","Mihir Sahasrabudhe","Alp Guler","Dimitris Samaras","Nikos Paragios","Iasonas Kokkinos"],"abstract":"In this work we introduce Deforming Autoencoders, a generative model for\nimages that disentangles shape from appearance in an unsupervised manner. As in\nthe deformable template paradigm, shape is represented as a deformation between\na canonical coordinate system (`template') and an observed image, while\nappearance is modeled in `canonical', template, coordinates, thus discarding\nvariability due to deformations. We introduce novel techniques that allow this\napproach to be deployed in the setting of autoencoders and show that this\nmethod can be used for unsupervised group-wise image alignment. We show\nexperiments with expression morphing in humans, hands, and digits, face\nmanipulation, such as shape and appearance interpolation, as well as\nunsupervised landmark localization. A more powerful form of unsupervised\ndisentangling becomes possible in template coordinates, allowing us to\nsuccessfully decompose face images into shading and albedo, and further\nmanipulate face images.","url_abs":"http://arxiv.org/abs/1806.06503v1","url_pdf":"http://arxiv.org/pdf/1806.06503v1.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":"deforming-autoencoders-unsupervised","repo_url":"https://github.com/stergioc/smooth-transformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deforming-autoencoders-unsupervised","repo_url":"https://github.com/zhixinshu/DeformingAutoencoders-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"unsupervised-facial-landmark-detection","task_name":"Unsupervised Facial Landmark Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-facial-landmark-detection-on-1","task":"Unsupervised Facial Landmark Detection","dataset":"MAFL","model":"Deforming Autoencoders","rank_in_archive_order":9,"of":13,"metrics":{"NME":"5.45"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.06503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.06503"}},"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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