{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:EGOB5LIWABYAWPFUVKFQPTXNK3","short_pith_number":"pith:EGOB5LIW","schema_version":"1.0","canonical_sha256":"219c1ead1600700b3cb4aa8b07ceed56f86d90af7af9d10727cbf4bd6347be6c","source":{"kind":"arxiv","id":"2201.06701","version":4},"attestation_state":"computed","paper":{"title":"Motion Inbetweening via Deep $\\Delta$-Interpolator","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antonios Valkanas, Boris N. Oreshkin, F\\'elix G. Harvey, Florent Bocquelet, Louis-Simon M\\'enard, Mark J. Coates","submitted_at":"2022-01-18T02:13:30Z","abstract_excerpt":"We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the $\\Delta$-regime is viable with respect to the reference of the last known frame (also known as the zero-velocity model). This supports the more general conclusion th"},"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":"2201.06701","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-01-18T02:13:30Z","cross_cats_sorted":[],"title_canon_sha256":"66f4a4ff063bb67759abecb973080056f91886a0c79684d4d3a497ae70af25ab","abstract_canon_sha256":"67f3a09faeceef9dabb8e4aa277389cc6857d795d1342196c45fab7eaec07850"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:11.215149Z","signature_b64":"lcndyVP9mDSy/f0kRIF/b4ZxWEhy/g53/XX347XZJYsMKBj7saPXGiIsosVxvsdfVP/uDa7xY47HNfeno+JDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"219c1ead1600700b3cb4aa8b07ceed56f86d90af7af9d10727cbf4bd6347be6c","last_reissued_at":"2026-07-05T04:49:11.214705Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:11.214705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Motion Inbetweening via Deep $\\Delta$-Interpolator","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antonios Valkanas, Boris N. Oreshkin, F\\'elix G. Harvey, Florent Bocquelet, Louis-Simon M\\'enard, Mark J. Coates","submitted_at":"2022-01-18T02:13:30Z","abstract_excerpt":"We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the $\\Delta$-regime is viable with respect to the reference of the last known frame (also known as the zero-velocity model). This supports the more general conclusion th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.06701","kind":"arxiv","version":4},"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/2201.06701/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":"2201.06701","created_at":"2026-07-05T04:49:11.214765+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.06701v4","created_at":"2026-07-05T04:49:11.214765+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.06701","created_at":"2026-07-05T04:49:11.214765+00:00"},{"alias_kind":"pith_short_12","alias_value":"EGOB5LIWABYA","created_at":"2026-07-05T04:49:11.214765+00:00"},{"alias_kind":"pith_short_16","alias_value":"EGOB5LIWABYAWPFU","created_at":"2026-07-05T04:49:11.214765+00:00"},{"alias_kind":"pith_short_8","alias_value":"EGOB5LIW","created_at":"2026-07-05T04:49:11.214765+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":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3","json":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3.json","graph_json":"https://pith.science/api/pith-number/EGOB5LIWABYAWPFUVKFQPTXNK3/graph.json","events_json":"https://pith.science/api/pith-number/EGOB5LIWABYAWPFUVKFQPTXNK3/events.json","paper":"https://pith.science/paper/EGOB5LIW"},"agent_actions":{"view_html":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3","download_json":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3.json","view_paper":"https://pith.science/paper/EGOB5LIW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.06701&json=true","fetch_graph":"https://pith.science/api/pith-number/EGOB5LIWABYAWPFUVKFQPTXNK3/graph.json","fetch_events":"https://pith.science/api/pith-number/EGOB5LIWABYAWPFUVKFQPTXNK3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3/action/storage_attestation","attest_author":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3/action/author_attestation","sign_citation":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3/action/citation_signature","submit_replication":"https://pith.science/pith/EGOB5LIWABYAWPFUVKFQPTXNK3/action/replication_record"}},"created_at":"2026-07-05T04:49:11.214765+00:00","updated_at":"2026-07-05T04:49:11.214765+00:00"}