{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:W7ONPL3EOCYX7EI745UODG5SSG","short_pith_number":"pith:W7ONPL3E","schema_version":"1.0","canonical_sha256":"b7dcd7af6470b17f911fe768e19bb29189b740cd49070a6ad3fa2284069111a1","source":{"kind":"arxiv","id":"2010.11531","version":1},"attestation_state":"computed","paper":{"title":"Convolutional Autoencoders for Human Motion Infilling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Emre Aksan, Fabrizio Pece, Jie Song, Manuel Kaufmann, Otmar Hilliges, Remo Ziegler","submitted_at":"2020-10-22T08:45:38Z","abstract_excerpt":"In this paper we propose a convolutional autoencoder to address the problem of motion infilling for 3D human motion data. Given a start and end sequence, motion infilling aims to complete the missing gap in between, such that the filled in poses plausibly forecast the start sequence and naturally transition into the end sequence. To this end, we propose a single, end-to-end trainable convolutional autoencoder. We show that a single model can be used to create natural transitions between different types of activities. Furthermore, our method is not only able to fill in entire missing frames, bu"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2010.11531","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-10-22T08:45:38Z","cross_cats_sorted":[],"title_canon_sha256":"0a6f390d60aafd2c1093e6f4a624b8abb136c588f859e716352c1d88c8ee835f","abstract_canon_sha256":"b2dc8749b1df5088b82fae70caf743ac8e746f826e96cf0952c3cae3df6f6aab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:31:54.509849Z","signature_b64":"nL6LYt4/T5PxD1YqPLF0Tb+DCFBC3r8yP67Zhg1GzoCw5cufYqO3pfmDyXCJR17bxacgvF9yfHE5oQ/VUZ4PCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7dcd7af6470b17f911fe768e19bb29189b740cd49070a6ad3fa2284069111a1","last_reissued_at":"2026-07-05T03:31:54.509311Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:31:54.509311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convolutional Autoencoders for Human Motion Infilling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Emre Aksan, Fabrizio Pece, Jie Song, Manuel Kaufmann, Otmar Hilliges, Remo Ziegler","submitted_at":"2020-10-22T08:45:38Z","abstract_excerpt":"In this paper we propose a convolutional autoencoder to address the problem of motion infilling for 3D human motion data. Given a start and end sequence, motion infilling aims to complete the missing gap in between, such that the filled in poses plausibly forecast the start sequence and naturally transition into the end sequence. To this end, we propose a single, end-to-end trainable convolutional autoencoder. We show that a single model can be used to create natural transitions between different types of activities. Furthermore, our method is not only able to fill in entire missing frames, bu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.11531","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.11531/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2010.11531","created_at":"2026-07-05T03:31:54.509386+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.11531v1","created_at":"2026-07-05T03:31:54.509386+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.11531","created_at":"2026-07-05T03:31:54.509386+00:00"},{"alias_kind":"pith_short_12","alias_value":"W7ONPL3EOCYX","created_at":"2026-07-05T03:31:54.509386+00:00"},{"alias_kind":"pith_short_16","alias_value":"W7ONPL3EOCYX7EI7","created_at":"2026-07-05T03:31:54.509386+00:00"},{"alias_kind":"pith_short_8","alias_value":"W7ONPL3E","created_at":"2026-07-05T03:31:54.509386+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22131","citing_title":"Feed-forward Motion In-betweening for Any 4D","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG","json":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG.json","graph_json":"https://pith.science/api/pith-number/W7ONPL3EOCYX7EI745UODG5SSG/graph.json","events_json":"https://pith.science/api/pith-number/W7ONPL3EOCYX7EI745UODG5SSG/events.json","paper":"https://pith.science/paper/W7ONPL3E"},"agent_actions":{"view_html":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG","download_json":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG.json","view_paper":"https://pith.science/paper/W7ONPL3E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.11531&json=true","fetch_graph":"https://pith.science/api/pith-number/W7ONPL3EOCYX7EI745UODG5SSG/graph.json","fetch_events":"https://pith.science/api/pith-number/W7ONPL3EOCYX7EI745UODG5SSG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG/action/storage_attestation","attest_author":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG/action/author_attestation","sign_citation":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG/action/citation_signature","submit_replication":"https://pith.science/pith/W7ONPL3EOCYX7EI745UODG5SSG/action/replication_record"}},"created_at":"2026-07-05T03:31:54.509386+00:00","updated_at":"2026-07-05T03:31:54.509386+00:00"}