{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:N4P4VXMLSSF5VA7S3N5DXLWGCU","short_pith_number":"pith:N4P4VXML","schema_version":"1.0","canonical_sha256":"6f1fcadd8b948bda83f2db7a3baec6151b37c5816a95e4a72ca604ed913e6367","source":{"kind":"arxiv","id":"2410.06014","version":1},"attestation_state":"computed","paper":{"title":"SplaTraj: Camera Trajectory Generation with Semantic Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Matthew Johnson-Roberson, Tianyi Zhang, Weiming Zhi, Xinyi Liu","submitted_at":"2024-10-08T13:16:49Z","abstract_excerpt":"Many recent developments for robots to represent environments have focused on photorealistic reconstructions. This paper particularly focuses on generating sequences of images from the photorealistic Gaussian Splatting models, that match instructions that are given by user-inputted language. We contribute a novel framework, SplaTraj, which formulates the generation of images within photorealistic environment representations as a continuous-time trajectory optimization problem. Costs are designed so that a camera following the trajectory poses will smoothly traverse through the environment and "},"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":"2410.06014","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-08T13:16:49Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"2bf2a54535a3c6f5d00e3b48a6048d0f69f470ce3edb5cc12cf577217470dbb5","abstract_canon_sha256":"02595f94fd8c030e50aab33f6eedbc6c0e92cf293fa71e2aaa226561f13f58dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:54.609865Z","signature_b64":"Vxahhiud/jburgmuM+EqSoIDs0ZRKKU0xe5hy8Ki7noyI8CMBhtBMl7uPsi46xZq/Hld0VtdoT6dJT9RtuFOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f1fcadd8b948bda83f2db7a3baec6151b37c5816a95e4a72ca604ed913e6367","last_reissued_at":"2026-07-05T09:17:54.609395Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:54.609395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SplaTraj: Camera Trajectory Generation with Semantic Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Matthew Johnson-Roberson, Tianyi Zhang, Weiming Zhi, Xinyi Liu","submitted_at":"2024-10-08T13:16:49Z","abstract_excerpt":"Many recent developments for robots to represent environments have focused on photorealistic reconstructions. This paper particularly focuses on generating sequences of images from the photorealistic Gaussian Splatting models, that match instructions that are given by user-inputted language. We contribute a novel framework, SplaTraj, which formulates the generation of images within photorealistic environment representations as a continuous-time trajectory optimization problem. Costs are designed so that a camera following the trajectory poses will smoothly traverse through the environment and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06014","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/2410.06014/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":"2410.06014","created_at":"2026-07-05T09:17:54.609448+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.06014v1","created_at":"2026-07-05T09:17:54.609448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.06014","created_at":"2026-07-05T09:17:54.609448+00:00"},{"alias_kind":"pith_short_12","alias_value":"N4P4VXMLSSF5","created_at":"2026-07-05T09:17:54.609448+00:00"},{"alias_kind":"pith_short_16","alias_value":"N4P4VXMLSSF5VA7S","created_at":"2026-07-05T09:17:54.609448+00:00"},{"alias_kind":"pith_short_8","alias_value":"N4P4VXML","created_at":"2026-07-05T09:17:54.609448+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.09862","citing_title":"FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU","json":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU.json","graph_json":"https://pith.science/api/pith-number/N4P4VXMLSSF5VA7S3N5DXLWGCU/graph.json","events_json":"https://pith.science/api/pith-number/N4P4VXMLSSF5VA7S3N5DXLWGCU/events.json","paper":"https://pith.science/paper/N4P4VXML"},"agent_actions":{"view_html":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU","download_json":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU.json","view_paper":"https://pith.science/paper/N4P4VXML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.06014&json=true","fetch_graph":"https://pith.science/api/pith-number/N4P4VXMLSSF5VA7S3N5DXLWGCU/graph.json","fetch_events":"https://pith.science/api/pith-number/N4P4VXMLSSF5VA7S3N5DXLWGCU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU/action/storage_attestation","attest_author":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU/action/author_attestation","sign_citation":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU/action/citation_signature","submit_replication":"https://pith.science/pith/N4P4VXMLSSF5VA7S3N5DXLWGCU/action/replication_record"}},"created_at":"2026-07-05T09:17:54.609448+00:00","updated_at":"2026-07-05T09:17:54.609448+00:00"}