{"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/deep-implicit-templates-for-3d-shape","title":"Deep Implicit Templates for 3D Shape Representation","arxiv_id":"2011.14565","date":"2020-11-30","proceeding":"CVPR 2021 1","authors":["Zerong Zheng","Tao Yu","Qionghai Dai","Yebin Liu"],"abstract":"Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power. However, unlike polygon mesh-based templates, it remains a challenge to reason dense correspondences or other semantic relationships across shapes represented by DIFs, which limits its applications in texture transfer, shape analysis and so on. To overcome this limitation and also make DIFs more interpretable, we propose Deep Implicit Templates, a new 3D shape representation that supports explicit correspondence reasoning in deep implicit representations. Our key idea is to formulate DIFs as conditional deformations of a template implicit function. To this end, we propose Spatial Warping LSTM, which decomposes the conditional spatial transformation into multiple affine transformations and guarantees generalization capability. Moreover, the training loss is carefully designed in order to achieve high reconstruction accuracy while learning a plausible template with accurate correspondences in an unsupervised manner. Experiments show that our method can not only learn a common implicit template for a collection of shapes, but also establish dense correspondences across all the shapes simultaneously without any supervision.","url_abs":"https://arxiv.org/abs/2011.14565v2","url_pdf":"https://arxiv.org/pdf/2011.14565v2.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":"deep-implicit-templates-for-3d-shape","repo_url":"https://github.com/ZhengZerong/DeepImplicitTemplates","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-shape-representation","task_name":"3D Shape Representation"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2011.14565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.14565"}},"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/ZhengZerong/DeepImplicitTemplates","reach":null}],"summary":{"ran_draft_wrong":1,"ran_honours":2},"by_repo_kind":{"official":{"samples":3,"ran":3,"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":3,"samples":[{"code_sha256_prefix":"bc6c7245b01ee009","entry":"apply_curriculum_l1_loss","repo":"ZhengZerong/DeepImplicitTemplates","repo_kind":"official","path":"train_deep_implicit_templates.py","file_url":"https://github.com/ZhengZerong/DeepImplicitTemplates/blob/HEAD/train_deep_implicit_templates.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bc6c7245b01ee009"}},{"code_sha256_prefix":"7815d3f627eec06b","entry":"get_mean_latent_vector_magnitude","repo":"ZhengZerong/DeepImplicitTemplates","repo_kind":"official","path":"train_deep_implicit_templates.py","file_url":"https://github.com/ZhengZerong/DeepImplicitTemplates/blob/HEAD/train_deep_implicit_templates.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7815d3f627eec06b"}},{"code_sha256_prefix":"b5c4c7182c531f7c","entry":"get_spec_with_default","repo":"ZhengZerong/DeepImplicitTemplates","repo_kind":"official","path":"train_deep_implicit_templates.py","file_url":"https://github.com/ZhengZerong/DeepImplicitTemplates/blob/HEAD/train_deep_implicit_templates.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b5c4c7182c531f7c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}