{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:P5M7BBACCOTIRNJXF2WMC2RRZG","short_pith_number":"pith:P5M7BBAC","schema_version":"1.0","canonical_sha256":"7f59f0840213a688b5372eacc16a31c9bebbd54b642618540d4145f3bb5c2520","source":{"kind":"arxiv","id":"2408.13953","version":1},"attestation_state":"computed","paper":{"title":"InterTrack: Tracking Human Object Interaction without Object Templates","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gerard Pons-Moll, Jan Eric Lenssen, Xianghui Xie","submitted_at":"2024-08-25T22:26:46Z","abstract_excerpt":"Tracking human object interaction from videos is important to understand human behavior from the rapidly growing stream of video data. Previous video-based methods require predefined object templates while single-image-based methods are template-free but lack temporal consistency. In this paper, we present a method to track human object interaction without any object shape templates. We decompose the 4D tracking problem into per-frame pose tracking and canonical shape optimization. We first apply a single-view reconstruction method to obtain temporally-inconsistent per-frame interaction recons"},"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":"2408.13953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-25T22:26:46Z","cross_cats_sorted":[],"title_canon_sha256":"df088959e3f35a56e01cb0074f99a6f787c774f5c0cec796aa9011d6481f5ed9","abstract_canon_sha256":"c0852a6f0f1e99706260aebf14a0e203b54e38fa01d9054fed63d4176ab80b02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:59:14.089406Z","signature_b64":"huXmh0hLeToexgpB0NGIMYwAOgk+1NIpyfzw81qPUQLvAKl9Hvg5rbJkHE6hT2HlfpIJQlDCrplj2KTMJP1ZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f59f0840213a688b5372eacc16a31c9bebbd54b642618540d4145f3bb5c2520","last_reissued_at":"2026-07-05T08:59:14.089052Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:59:14.089052Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"InterTrack: Tracking Human Object Interaction without Object Templates","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gerard Pons-Moll, Jan Eric Lenssen, Xianghui Xie","submitted_at":"2024-08-25T22:26:46Z","abstract_excerpt":"Tracking human object interaction from videos is important to understand human behavior from the rapidly growing stream of video data. Previous video-based methods require predefined object templates while single-image-based methods are template-free but lack temporal consistency. In this paper, we present a method to track human object interaction without any object shape templates. We decompose the 4D tracking problem into per-frame pose tracking and canonical shape optimization. We first apply a single-view reconstruction method to obtain temporally-inconsistent per-frame interaction recons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.13953","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/2408.13953/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":"2408.13953","created_at":"2026-07-05T08:59:14.089108+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.13953v1","created_at":"2026-07-05T08:59:14.089108+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.13953","created_at":"2026-07-05T08:59:14.089108+00:00"},{"alias_kind":"pith_short_12","alias_value":"P5M7BBACCOTI","created_at":"2026-07-05T08:59:14.089108+00:00"},{"alias_kind":"pith_short_16","alias_value":"P5M7BBACCOTIRNJX","created_at":"2026-07-05T08:59:14.089108+00:00"},{"alias_kind":"pith_short_8","alias_value":"P5M7BBAC","created_at":"2026-07-05T08:59:14.089108+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.21351","citing_title":"Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG","json":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG.json","graph_json":"https://pith.science/api/pith-number/P5M7BBACCOTIRNJXF2WMC2RRZG/graph.json","events_json":"https://pith.science/api/pith-number/P5M7BBACCOTIRNJXF2WMC2RRZG/events.json","paper":"https://pith.science/paper/P5M7BBAC"},"agent_actions":{"view_html":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG","download_json":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG.json","view_paper":"https://pith.science/paper/P5M7BBAC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.13953&json=true","fetch_graph":"https://pith.science/api/pith-number/P5M7BBACCOTIRNJXF2WMC2RRZG/graph.json","fetch_events":"https://pith.science/api/pith-number/P5M7BBACCOTIRNJXF2WMC2RRZG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG/action/storage_attestation","attest_author":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG/action/author_attestation","sign_citation":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG/action/citation_signature","submit_replication":"https://pith.science/pith/P5M7BBACCOTIRNJXF2WMC2RRZG/action/replication_record"}},"created_at":"2026-07-05T08:59:14.089108+00:00","updated_at":"2026-07-05T08:59:14.089108+00:00"}