{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:U2HX4VYQUXWJEOUREUTJHV3J4S","short_pith_number":"pith:U2HX4VYQ","schema_version":"1.0","canonical_sha256":"a68f7e5710a5ec923a91252693d769e480dffc5acc2daacddd3b49fadacf9881","source":{"kind":"arxiv","id":"1708.02970","version":1},"attestation_state":"computed","paper":{"title":"Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Baoyuan Wang, In So Kweon, Kyungdon Joo, Neel Joshi, Sing Bing Kang, Tae-Hyun Oh","submitted_at":"2017-08-09T19:03:12Z","abstract_excerpt":"Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts (such a tearing). To achieve this, we present a new technique that uses object recognition and semantic segmentation as part of an optimization method to automatically create cinemagraphs from videos"},"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":"1708.02970","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-09T19:03:12Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"b442e1b5b6a0b6f05081fae9525da9310494e0f5cd26ac34b2c08dff21536113","abstract_canon_sha256":"db124f9b818a08c729630bdf36e5fa859eb41acc04cc86e3ff0d6514db36afa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:17.603723Z","signature_b64":"kjgiqAsF/RnS8T52UeGcMFBRe0erZV3RrsAoFRAVyehp4kFnwJu+eeOI9yqRXjWyyg4FYjSOXNpomY22CYDtDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a68f7e5710a5ec923a91252693d769e480dffc5acc2daacddd3b49fadacf9881","last_reissued_at":"2026-05-18T00:38:17.603066Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:17.603066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Baoyuan Wang, In So Kweon, Kyungdon Joo, Neel Joshi, Sing Bing Kang, Tae-Hyun Oh","submitted_at":"2017-08-09T19:03:12Z","abstract_excerpt":"Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts (such a tearing). To achieve this, we present a new technique that uses object recognition and semantic segmentation as part of an optimization method to automatically create cinemagraphs from videos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02970","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":""},"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":"1708.02970","created_at":"2026-05-18T00:38:17.603201+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.02970v1","created_at":"2026-05-18T00:38:17.603201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02970","created_at":"2026-05-18T00:38:17.603201+00:00"},{"alias_kind":"pith_short_12","alias_value":"U2HX4VYQUXWJ","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"U2HX4VYQUXWJEOUR","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"U2HX4VYQ","created_at":"2026-05-18T12:31:46.661854+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/U2HX4VYQUXWJEOUREUTJHV3J4S","json":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S.json","graph_json":"https://pith.science/api/pith-number/U2HX4VYQUXWJEOUREUTJHV3J4S/graph.json","events_json":"https://pith.science/api/pith-number/U2HX4VYQUXWJEOUREUTJHV3J4S/events.json","paper":"https://pith.science/paper/U2HX4VYQ"},"agent_actions":{"view_html":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S","download_json":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S.json","view_paper":"https://pith.science/paper/U2HX4VYQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.02970&json=true","fetch_graph":"https://pith.science/api/pith-number/U2HX4VYQUXWJEOUREUTJHV3J4S/graph.json","fetch_events":"https://pith.science/api/pith-number/U2HX4VYQUXWJEOUREUTJHV3J4S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S/action/storage_attestation","attest_author":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S/action/author_attestation","sign_citation":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S/action/citation_signature","submit_replication":"https://pith.science/pith/U2HX4VYQUXWJEOUREUTJHV3J4S/action/replication_record"}},"created_at":"2026-05-18T00:38:17.603201+00:00","updated_at":"2026-05-18T00:38:17.603201+00:00"}