{"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/mapconnet-self-supervised-3d-pose-transfer","title":"MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning","arxiv_id":"2304.13819","date":"2023-04-26","proceeding":"ICCV 2023 1","authors":["Jiaze Sun","Zhixiang Chen","Tae-Kyun Kim"],"abstract":"3D pose transfer is a challenging generation task that aims to transfer the pose of a source geometry onto a target geometry with the target identity preserved. Many prior methods require keypoint annotations to find correspondence between the source and target. Current pose transfer methods allow end-to-end correspondence learning but require the desired final output as ground truth for supervision. Unsupervised methods have been proposed for graph convolutional models but they require ground truth correspondence between the source and target inputs. We present a novel self-supervised framework for 3D pose transfer which can be trained in unsupervised, semi-supervised, or fully supervised settings without any correspondence labels. We introduce two contrastive learning constraints in the latent space: a mesh-level loss for disentangling global patterns including pose and identity, and a point-level loss for discriminating local semantics. We demonstrate quantitatively and qualitatively that our method achieves state-of-the-art results in supervised 3D pose transfer, with comparable results in unsupervised and semi-supervised settings. Our method is also generalisable to unseen human and animal data with complex topologies.","url_abs":"https://arxiv.org/abs/2304.13819v2","url_pdf":"https://arxiv.org/pdf/2304.13819v2.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":"mapconnet-self-supervised-3d-pose-transfer","repo_url":"https://github.com/justin941208/mapconnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"pose-transfer","task_name":"Pose Transfer"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2304.13819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13819"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/justin941208/mapconnet","reach":null}],"summary":{"ran":4,"ran_honours":1},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"a106ad7f35386d6d","entry":"AdaptiveFeatureGenerator","repo":"justin941208/mapconnet","repo_kind":"official","path":"models/networks/correspondence.py","file_url":"https://github.com/justin941208/mapconnet/blob/HEAD/models/networks/correspondence.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"a106ad7f35386d6d"}},{"code_sha256_prefix":"903a1c9c2003c504","entry":"BaseNetwork","repo":"justin941208/mapconnet","repo_kind":"official","path":"models/networks/correspondence.py","file_url":"https://github.com/justin941208/mapconnet/blob/HEAD/models/networks/correspondence.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"903a1c9c2003c504"}},{"code_sha256_prefix":"3a33d763c5278339","entry":"Correspondence","repo":"justin941208/mapconnet","repo_kind":"official","path":"models/networks/correspondence.py","file_url":"https://github.com/justin941208/mapconnet/blob/HEAD/models/networks/correspondence.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"3a33d763c5278339"}},{"code_sha256_prefix":"c60a1a282d8fee63","entry":"ResidualBlock","repo":"justin941208/mapconnet","repo_kind":"official","path":"models/networks/correspondence.py","file_url":"https://github.com/justin941208/mapconnet/blob/HEAD/models/networks/correspondence.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"c60a1a282d8fee63"}},{"code_sha256_prefix":"8620e83e8074e290","entry":"feature_normalize","repo":"justin941208/mapconnet","repo_kind":"official","path":"models/networks/correspondence.py","file_url":"https://github.com/justin941208/mapconnet/blob/HEAD/models/networks/correspondence.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"8620e83e8074e290"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}