{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RHYMM7DJNYEVPYRLASH6R7KDPW","short_pith_number":"pith:RHYMM7DJ","schema_version":"1.0","canonical_sha256":"89f0c67c696e0957e22b048fe8fd437d984d0554d7ccb3530eaadb50805077c1","source":{"kind":"arxiv","id":"2605.31595","version":1},"attestation_state":"computed","paper":{"title":"Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Donghwan Shin, Honggyu An, Hyeonseo Yu, Hyuna Ko, Jaewoo Jung, Jisang Han, Kazumi Fukuda, Minkyeong Jeon, Mungyeom Kim, Seungryong Kim, Sunghwan Hong, Takuya Narihira, Yuki Mitsufuji","submitted_at":"2026-05-29T17:57:41Z","abstract_excerpt":"Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame, suffering from duplicated Gaussians and view-dependent biases that hinder effective learning of scene motion. We present C4G, a feed-forward 4D reconstruction framework built upon a compact set of timestamp-conditioned learnable Gaussian query tokens. Each token aggregates corresponding features across the full temporal context and decodes a 3D Gaussian whose position is modulated by the target timestamp, enabling gl"},"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":"2605.31595","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T17:57:41Z","cross_cats_sorted":[],"title_canon_sha256":"b97b6f5838389412a44d1daa3bd24244a2a38becd362d6a870293f796da0bc27","abstract_canon_sha256":"a85e7551e9bab77cf59989985dfd49556a617d584151e014df2b7fdc687f9dae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T02:04:14.997237Z","signature_b64":"zVxnPOpowOlaXtn5r7y3TDGXS1bpJ8JIRAL1l5hl7UrQErATt6WCcof15yJWMFI2afm4jNvsGBkw4EqgEz30CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89f0c67c696e0957e22b048fe8fd437d984d0554d7ccb3530eaadb50805077c1","last_reissued_at":"2026-06-01T02:04:14.996479Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T02:04:14.996479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Donghwan Shin, Honggyu An, Hyeonseo Yu, Hyuna Ko, Jaewoo Jung, Jisang Han, Kazumi Fukuda, Minkyeong Jeon, Mungyeom Kim, Seungryong Kim, Sunghwan Hong, Takuya Narihira, Yuki Mitsufuji","submitted_at":"2026-05-29T17:57:41Z","abstract_excerpt":"Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame, suffering from duplicated Gaussians and view-dependent biases that hinder effective learning of scene motion. We present C4G, a feed-forward 4D reconstruction framework built upon a compact set of timestamp-conditioned learnable Gaussian query tokens. Each token aggregates corresponding features across the full temporal context and decodes a 3D Gaussian whose position is modulated by the target timestamp, enabling gl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31595","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/2605.31595/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":"2605.31595","created_at":"2026-06-01T02:04:14.996606+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31595v1","created_at":"2026-06-01T02:04:14.996606+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31595","created_at":"2026-06-01T02:04:14.996606+00:00"},{"alias_kind":"pith_short_12","alias_value":"RHYMM7DJNYEV","created_at":"2026-06-01T02:04:14.996606+00:00"},{"alias_kind":"pith_short_16","alias_value":"RHYMM7DJNYEVPYRL","created_at":"2026-06-01T02:04:14.996606+00:00"},{"alias_kind":"pith_short_8","alias_value":"RHYMM7DJ","created_at":"2026-06-01T02:04:14.996606+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/RHYMM7DJNYEVPYRLASH6R7KDPW","json":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW.json","graph_json":"https://pith.science/api/pith-number/RHYMM7DJNYEVPYRLASH6R7KDPW/graph.json","events_json":"https://pith.science/api/pith-number/RHYMM7DJNYEVPYRLASH6R7KDPW/events.json","paper":"https://pith.science/paper/RHYMM7DJ"},"agent_actions":{"view_html":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW","download_json":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW.json","view_paper":"https://pith.science/paper/RHYMM7DJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31595&json=true","fetch_graph":"https://pith.science/api/pith-number/RHYMM7DJNYEVPYRLASH6R7KDPW/graph.json","fetch_events":"https://pith.science/api/pith-number/RHYMM7DJNYEVPYRLASH6R7KDPW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW/action/storage_attestation","attest_author":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW/action/author_attestation","sign_citation":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW/action/citation_signature","submit_replication":"https://pith.science/pith/RHYMM7DJNYEVPYRLASH6R7KDPW/action/replication_record"}},"created_at":"2026-06-01T02:04:14.996606+00:00","updated_at":"2026-06-01T02:04:14.996606+00:00"}