{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YIOYEVFXNNF366DXFWAKO4ARL7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4605331e552470134c3497372c690618e7b1cb591dc597b968a66c9f329de0f7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:26:25Z","title_canon_sha256":"d9dd2e621add870c13d2b5b636e864174df5e55b1d36a2c1a3d6fdab2fff61c1"},"schema_version":"1.0","source":{"id":"2605.13591","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13591","created_at":"2026-05-18T02:44:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13591v1","created_at":"2026-05-18T02:44:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13591","created_at":"2026-05-18T02:44:23Z"},{"alias_kind":"pith_short_12","alias_value":"YIOYEVFXNNF3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"YIOYEVFXNNF366DX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"YIOYEVFX","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:50f13a96c987e7ac6a58af86fd34ff705937894224c3b7ede64b67500bd5ffaf","target":"graph","created_at":"2026-05-18T02:44:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Real2Sim explicitly reconstructs dynamic driving scenes as temporally continuous Gaussian primitives, supports instance-level editing, and simulates realistic object-object and object-environment interactions. This framework enables physics-aware, high-fidelity synthesis of diverse, editable scenarios, including challenging corner cases such as collisions and post-impact trajectories."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the differentiable MPM solver can be tightly coupled with 4D Gaussian Splatting without introducing visual artifacts, temporal inconsistencies, or loss of physical accuracy when applied to complex real-world driving data."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A framework fuses 4D Gaussian Splatting with a physics solver to reconstruct and edit dynamic driving scenes while preserving realistic interactions."}],"snapshot_sha256":"eec23c91314e99fc4215488e65569f4e695d113bd0f1123a269709f93f2b6112"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Reliable autonomous driving relies on large-scale, well-labeled data and robust models. However, manual data collection is resource-intensive, and traditional simulation suffers from a persistent reality gap. While recent generative frameworks and radiance-field methods improve visual fidelity, they still struggle with temporal and spatial consistency and cannot ensure physics-aware behavior, limiting their applicability to driving scenario generation. To address these challenges, we propose Real2Sim, an unified framework that combines 4D Gaussian Splatting (4DGS) with a differentiable Materia","authors_text":"Kaicong Huang, Ruimin Ke, Talha Azfar, Weisong Shi","cross_cats":[],"headline":"A framework fuses 4D Gaussian Splatting with a physics solver to reconstruct and edit dynamic driving scenes while preserving realistic interactions.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:26:25Z","title":"Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes"},"references":{"count":27,"internal_anchors":1,"resolved_work":27,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Scalability in perception for autonomous driving: Waymo open dataset","work_id":"cf5ae3fc-bc37-46a5-87ce-fb9a52cded92","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Surfelgan: Synthesizing realistic sensor data for autonomous driving,","work_id":"35ce85bd-54df-4f8e-8e16-783f5310491f","year":2020},{"cited_arxiv_id":"2503.20523","doi":"","is_internal_anchor":true,"ref_index":3,"title":"GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving","work_id":"1339e674-d09b-48b4-8e6f-efe55dcab22e","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Nerf: Representing scenes as neural radiance fields for view synthesis","work_id":"b42b10ce-3678-4f2e-bf7e-da503030acf8","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"3d gaussian splatting for real-time radiance field rendering","work_id":"698160da-5666-42e0-b122-4a7c98e53e9b","year":2023}],"snapshot_sha256":"e77d70e3be50e577b4bb7c39383f43f4f5e944f3bd11083244b48b3ddc2864ab"},"source":{"id":"2605.13591","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T19:27:01.406120Z","id":"a2759f60-d943-40e2-b3d8-fdd7e39df208","model_set":{"reader":"grok-4.3"},"one_line_summary":"Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A framework fuses 4D Gaussian Splatting with a physics solver to reconstruct and edit dynamic driving scenes while preserving realistic interactions.","strongest_claim":"Real2Sim explicitly reconstructs dynamic driving scenes as temporally continuous Gaussian primitives, supports instance-level editing, and simulates realistic object-object and object-environment interactions. This framework enables physics-aware, high-fidelity synthesis of diverse, editable scenarios, including challenging corner cases such as collisions and post-impact trajectories.","weakest_assumption":"That the differentiable MPM solver can be tightly coupled with 4D Gaussian Splatting without introducing visual artifacts, temporal inconsistencies, or loss of physical accuracy when applied to complex real-world driving data."}},"verdict_id":"a2759f60-d943-40e2-b3d8-fdd7e39df208"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f128c62b6f351718982938da42a15bf38be5298c079573f370dcd26570ab65e4","target":"record","created_at":"2026-05-18T02:44:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4605331e552470134c3497372c690618e7b1cb591dc597b968a66c9f329de0f7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:26:25Z","title_canon_sha256":"d9dd2e621add870c13d2b5b636e864174df5e55b1d36a2c1a3d6fdab2fff61c1"},"schema_version":"1.0","source":{"id":"2605.13591","kind":"arxiv","version":1}},"canonical_sha256":"c21d8254b76b4bbf78772d80a770115fd88c9eeed582dd655323a759ab665b60","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c21d8254b76b4bbf78772d80a770115fd88c9eeed582dd655323a759ab665b60","first_computed_at":"2026-05-18T02:44:23.066189Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:23.066189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"29IkL8SspI9dDhQhisgxbrl1pYcZG5HnZJ/kuzHrfgEfSd0+VkV18hYSQkAIQY75aJCLStcVbvqCXQ13sTtTAA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:23.066624Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13591","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f128c62b6f351718982938da42a15bf38be5298c079573f370dcd26570ab65e4","sha256:50f13a96c987e7ac6a58af86fd34ff705937894224c3b7ede64b67500bd5ffaf"],"state_sha256":"f334c52cbdf66904c1586e4d24374b5e7dde39ca3df5218d20d7e178bd19dc46"}