{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CNKO7ZPF2NRUIW6J4LIWRVDKLG","short_pith_number":"pith:CNKO7ZPF","canonical_record":{"source":{"id":"1812.02784","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-06T20:10:23Z","cross_cats_sorted":[],"title_canon_sha256":"b8358c88395420b8121b0318d24dc4426ad75dd014d863722337dbac7866898b","abstract_canon_sha256":"6bb33687c11b7f469dfdd79b743572e741ff229260a875dac1e5cfb90cbfe783"},"schema_version":"1.0"},"canonical_sha256":"1354efe5e5d363445bc9e2d168d46a59a4c5b63dc8549ba7761fbe849e1fce56","source":{"kind":"arxiv","id":"1812.02784","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.02784","created_at":"2026-05-17T23:48:16Z"},{"alias_kind":"arxiv_version","alias_value":"1812.02784v2","created_at":"2026-05-17T23:48:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.02784","created_at":"2026-05-17T23:48:16Z"},{"alias_kind":"pith_short_12","alias_value":"CNKO7ZPF2NRU","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CNKO7ZPF2NRUIW6J","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CNKO7ZPF","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CNKO7ZPF2NRUIW6J4LIWRVDKLG","target":"record","payload":{"canonical_record":{"source":{"id":"1812.02784","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-06T20:10:23Z","cross_cats_sorted":[],"title_canon_sha256":"b8358c88395420b8121b0318d24dc4426ad75dd014d863722337dbac7866898b","abstract_canon_sha256":"6bb33687c11b7f469dfdd79b743572e741ff229260a875dac1e5cfb90cbfe783"},"schema_version":"1.0"},"canonical_sha256":"1354efe5e5d363445bc9e2d168d46a59a4c5b63dc8549ba7761fbe849e1fce56","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:16.870657Z","signature_b64":"3oZayDe7kRM9Njh0MKhk3lm/sF3xkVV9l6toavE9ymdjCaETA+8tz58PABz5M5viS3hJi9Vssi4HxK57p5aTAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1354efe5e5d363445bc9e2d168d46a59a4c5b63dc8549ba7761fbe849e1fce56","last_reissued_at":"2026-05-17T23:48:16.870001Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:16.870001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.02784","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hxI5hNK5S2KJnXrci1grqKeXSj+ydhBdH1/fI7z8Pm9goWVG4vmEqRPYbyhQsmNtEYtOjirZUmkFw5JuLdtrBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:03:57.395378Z"},"content_sha256":"9c3977207c7ff67ea2d75a9b538fcc90865f32dedf8419c1ef49c05676ad3a8d","schema_version":"1.0","event_id":"sha256:9c3977207c7ff67ea2d75a9b538fcc90865f32dedf8419c1ef49c05676ad3a8d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CNKO7ZPF2NRUIW6J4LIWRVDKLG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"StoryGAN: A Sequential Conditional GAN for Story Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David Carlson, Jianfeng Gao, Jingjing Liu, Lawrence Carin, Yelong Shen, Yitong Li, Yu Cheng, Yuexin Wu, Zhe Gan","submitted_at":"2018-12-06T20:10:23Z","abstract_excerpt":"We propose a new task, called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast to video generation, story visualization focuses less on the continuity in generated images (frames), but more on the global consistency across dynamic scenes and characters -- a challenge that has not been addressed by any single-image or video generation methods. We therefore propose a new story-to-image-sequence generation model, StoryGAN, based on the sequential conditional GAN framework. Our model is unique in t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.02784","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OMUztxfy+2ryPKAjDLNMIODMjJkDgTEpTwujf4MBYvFPnRkBfksM+iPol9Pm88BG52LngEMoib3prmX6UJFQAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:03:57.395967Z"},"content_sha256":"8adf8fd5dbb77dcbec0eba0d7a78ab7ea211dc9e2d90218f7957fe529a8c6228","schema_version":"1.0","event_id":"sha256:8adf8fd5dbb77dcbec0eba0d7a78ab7ea211dc9e2d90218f7957fe529a8c6228"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CNKO7ZPF2NRUIW6J4LIWRVDKLG/bundle.json","state_url":"https://pith.science/pith/CNKO7ZPF2NRUIW6J4LIWRVDKLG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CNKO7ZPF2NRUIW6J4LIWRVDKLG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-27T03:03:57Z","links":{"resolver":"https://pith.science/pith/CNKO7ZPF2NRUIW6J4LIWRVDKLG","bundle":"https://pith.science/pith/CNKO7ZPF2NRUIW6J4LIWRVDKLG/bundle.json","state":"https://pith.science/pith/CNKO7ZPF2NRUIW6J4LIWRVDKLG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CNKO7ZPF2NRUIW6J4LIWRVDKLG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CNKO7ZPF2NRUIW6J4LIWRVDKLG","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":"6bb33687c11b7f469dfdd79b743572e741ff229260a875dac1e5cfb90cbfe783","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-06T20:10:23Z","title_canon_sha256":"b8358c88395420b8121b0318d24dc4426ad75dd014d863722337dbac7866898b"},"schema_version":"1.0","source":{"id":"1812.02784","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.02784","created_at":"2026-05-17T23:48:16Z"},{"alias_kind":"arxiv_version","alias_value":"1812.02784v2","created_at":"2026-05-17T23:48:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.02784","created_at":"2026-05-17T23:48:16Z"},{"alias_kind":"pith_short_12","alias_value":"CNKO7ZPF2NRU","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CNKO7ZPF2NRUIW6J","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CNKO7ZPF","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:8adf8fd5dbb77dcbec0eba0d7a78ab7ea211dc9e2d90218f7957fe529a8c6228","target":"graph","created_at":"2026-05-17T23:48:16Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We propose a new task, called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast to video generation, story visualization focuses less on the continuity in generated images (frames), but more on the global consistency across dynamic scenes and characters -- a challenge that has not been addressed by any single-image or video generation methods. We therefore propose a new story-to-image-sequence generation model, StoryGAN, based on the sequential conditional GAN framework. Our model is unique in t","authors_text":"David Carlson, Jianfeng Gao, Jingjing Liu, Lawrence Carin, Yelong Shen, Yitong Li, Yu Cheng, Yuexin Wu, Zhe Gan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-06T20:10:23Z","title":"StoryGAN: A Sequential Conditional GAN for Story Visualization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.02784","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9c3977207c7ff67ea2d75a9b538fcc90865f32dedf8419c1ef49c05676ad3a8d","target":"record","created_at":"2026-05-17T23:48:16Z","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":"6bb33687c11b7f469dfdd79b743572e741ff229260a875dac1e5cfb90cbfe783","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-06T20:10:23Z","title_canon_sha256":"b8358c88395420b8121b0318d24dc4426ad75dd014d863722337dbac7866898b"},"schema_version":"1.0","source":{"id":"1812.02784","kind":"arxiv","version":2}},"canonical_sha256":"1354efe5e5d363445bc9e2d168d46a59a4c5b63dc8549ba7761fbe849e1fce56","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1354efe5e5d363445bc9e2d168d46a59a4c5b63dc8549ba7761fbe849e1fce56","first_computed_at":"2026-05-17T23:48:16.870001Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:16.870001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3oZayDe7kRM9Njh0MKhk3lm/sF3xkVV9l6toavE9ymdjCaETA+8tz58PABz5M5viS3hJi9Vssi4HxK57p5aTAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:16.870657Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.02784","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9c3977207c7ff67ea2d75a9b538fcc90865f32dedf8419c1ef49c05676ad3a8d","sha256:8adf8fd5dbb77dcbec0eba0d7a78ab7ea211dc9e2d90218f7957fe529a8c6228"],"state_sha256":"9fa6e114ec4504275dca5d23bd68c8b70e4488447d4daed2f8fb27956db45f5d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"22DL0nQX+obSwywrhfTGN3/buzmHn57YGWiuBQGXcWkUnZ3MVUTVv3wl7FlKxNjiy9FbpDqHKiqf3iCylneYAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T03:03:57.398973Z","bundle_sha256":"8c06da95337175477663f21335a8ab5f48d2825d3121e7970407e178f98916af"}}