{"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/disentangled-representation-learning-for-3d","title":"Disentangled Representation Learning for 3D Face Shape","arxiv_id":"1902.09887","date":"2019-02-26","proceeding":"CVPR 2019 6","authors":["Zi-Hang Jiang","Qianyi Wu","Keyu Chen","Juyong Zhang"],"abstract":"In this paper, we present a novel strategy to design disentangled 3D face\nshape representation. Specifically, a given 3D face shape is decomposed into\nidentity part and expression part, which are both encoded and decoded in a\nnonlinear way. To solve this problem, we propose an attribute decomposition\nframework for 3D face mesh. To better represent face shapes which are usually\nnonlinear deformed between each other, the face shapes are represented by a\nvertex based deformation representation rather than Euclidean coordinates. The\nexperimental results demonstrate that our method has better performance than\nexisting methods on decomposing the identity and expression parts. Moreover,\nmore natural expression transfer results can be achieved with our method than\nexisting methods.","url_abs":"http://arxiv.org/abs/1902.09887v2","url_pdf":"http://arxiv.org/pdf/1902.09887v2.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":"disentangled-representation-learning-for-3d","repo_url":"https://github.com/zihangJiang/DR-Learning-for-3D-Face","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09887"}},"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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