{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HLLF547EVOWIWS4RSEX2OYJZ6J","short_pith_number":"pith:HLLF547E","schema_version":"1.0","canonical_sha256":"3ad65ef3e4abac8b4b91912fa76139f2672876c2cb90d99a20251a309788783b","source":{"kind":"arxiv","id":"2506.03118","version":1},"attestation_state":"computed","paper":{"title":"HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Can Yang, Hujun Bao, Xiaowei Zhou, Zhe Li, Zhiyuan Yu","submitted_at":"2025-06-03T17:50:05Z","abstract_excerpt":"3D human reconstruction and animation are long-standing topics in computer graphics and vision. However, existing methods typically rely on sophisticated dense-view capture and/or time-consuming per-subject optimization procedures. To address these limitations, we propose HumanRAM, a novel feed-forward approach for generalizable human reconstruction and animation from monocular or sparse human images. Our approach integrates human reconstruction and animation into a unified framework by introducing explicit pose conditions, parameterized by a shared SMPL-X neural texture, into transformer-base"},"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":"2506.03118","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-06-03T17:50:05Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"384f3bc25926871a9f3fa5372f28c6deb437805771b044206cda3e7a11de2f7e","abstract_canon_sha256":"f653ca37bc9035349c79a71590c47e2d302c950f3bb6a14d8d8d86216e6933c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:15.169748Z","signature_b64":"3d2nP8KAiy4T6hCQlxsUL/Ic1EHjk4S6qOz4QjzMVr5ZylmyO/xy1dYN0AD5rtEQp03RZlIzth3hr/9UaaPlCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ad65ef3e4abac8b4b91912fa76139f2672876c2cb90d99a20251a309788783b","last_reissued_at":"2026-07-05T11:15:15.169121Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:15.169121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Can Yang, Hujun Bao, Xiaowei Zhou, Zhe Li, Zhiyuan Yu","submitted_at":"2025-06-03T17:50:05Z","abstract_excerpt":"3D human reconstruction and animation are long-standing topics in computer graphics and vision. However, existing methods typically rely on sophisticated dense-view capture and/or time-consuming per-subject optimization procedures. To address these limitations, we propose HumanRAM, a novel feed-forward approach for generalizable human reconstruction and animation from monocular or sparse human images. Our approach integrates human reconstruction and animation into a unified framework by introducing explicit pose conditions, parameterized by a shared SMPL-X neural texture, into transformer-base"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03118","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/2506.03118/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":"2506.03118","created_at":"2026-07-05T11:15:15.169223+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.03118v1","created_at":"2026-07-05T11:15:15.169223+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03118","created_at":"2026-07-05T11:15:15.169223+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLLF547EVOWI","created_at":"2026-07-05T11:15:15.169223+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLLF547EVOWIWS4R","created_at":"2026-07-05T11:15:15.169223+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLLF547E","created_at":"2026-07-05T11:15:15.169223+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J","json":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J.json","graph_json":"https://pith.science/api/pith-number/HLLF547EVOWIWS4RSEX2OYJZ6J/graph.json","events_json":"https://pith.science/api/pith-number/HLLF547EVOWIWS4RSEX2OYJZ6J/events.json","paper":"https://pith.science/paper/HLLF547E"},"agent_actions":{"view_html":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J","download_json":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J.json","view_paper":"https://pith.science/paper/HLLF547E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.03118&json=true","fetch_graph":"https://pith.science/api/pith-number/HLLF547EVOWIWS4RSEX2OYJZ6J/graph.json","fetch_events":"https://pith.science/api/pith-number/HLLF547EVOWIWS4RSEX2OYJZ6J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J/action/storage_attestation","attest_author":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J/action/author_attestation","sign_citation":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J/action/citation_signature","submit_replication":"https://pith.science/pith/HLLF547EVOWIWS4RSEX2OYJZ6J/action/replication_record"}},"created_at":"2026-07-05T11:15:15.169223+00:00","updated_at":"2026-07-05T11:15:15.169223+00:00"}