{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4AKMMBRFJZECZWQBHBD2M7EVET","short_pith_number":"pith:4AKMMBRF","canonical_record":{"source":{"id":"2412.08573","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-11T17:41:53Z","cross_cats_sorted":[],"title_canon_sha256":"683e8c6e7af6be93bcbcbad7a4e61d5939839d4aeca397e191e249984ff62129","abstract_canon_sha256":"ba3d4f3931ff6c95de8a1f5f6c1c5672fe765e05016bb421b46840b0e89c3d2d"},"schema_version":"1.0"},"canonical_sha256":"e014c606254e482cda013847a67c9524f148248fd468f110ffca3613c7b8f1ec","source":{"kind":"arxiv","id":"2412.08573","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08573","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08573v2","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08573","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"pith_short_12","alias_value":"4AKMMBRFJZEC","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"pith_short_16","alias_value":"4AKMMBRFJZECZWQB","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"pith_short_8","alias_value":"4AKMMBRF","created_at":"2026-07-05T09:56:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4AKMMBRFJZECZWQBHBD2M7EVET","target":"record","payload":{"canonical_record":{"source":{"id":"2412.08573","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-11T17:41:53Z","cross_cats_sorted":[],"title_canon_sha256":"683e8c6e7af6be93bcbcbad7a4e61d5939839d4aeca397e191e249984ff62129","abstract_canon_sha256":"ba3d4f3931ff6c95de8a1f5f6c1c5672fe765e05016bb421b46840b0e89c3d2d"},"schema_version":"1.0"},"canonical_sha256":"e014c606254e482cda013847a67c9524f148248fd468f110ffca3613c7b8f1ec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:38.059190Z","signature_b64":"rFagxPEukn73W8dZpAmOjjaFpNOxQpe89gRO+Iv9bp8wZxuojJHAVdocu7DugM5x9mwZGMn26bZPzoX1ItjiAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e014c606254e482cda013847a67c9524f148248fd468f110ffca3613c7b8f1ec","last_reissued_at":"2026-07-05T09:56:38.058689Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:38.058689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.08573","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":"2026-07-05T09:56:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l0zxbZfEVFxGn6cFWyZiTc/DQoD91Pxa3FYly07YjcVA/ohuCbauX0kDD6xt60mHsMDpCnx21J13cE6LxUarDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:08:40.069481Z"},"content_sha256":"c570536f512dd992fe65e27c1677e26e5f95bde2df483ad7362d52154f3b6e8a","schema_version":"1.0","event_id":"sha256:c570536f512dd992fe65e27c1677e26e5f95bde2df483ad7362d52154f3b6e8a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4AKMMBRFJZECZWQBHBD2M7EVET","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TryOffAnyone: Tiled Cloth Generation from a Dressed Person","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ioannis Xarchakos, Theodoros Koukopoulos","submitted_at":"2024-12-11T17:41:53Z","abstract_excerpt":"The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challenge of generating high-fidelity tiled garment images essential for personalized recommendations, outfit composition, and virtual try-on systems from photos of garments worn by models. Inspired by the success of Latent Diffusion Models (LDMs) in image-to-image translation, we propose a novel approach utilizing a fine-tuned StableDiffusion model. Our method features a streamlined single-stage networ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08573","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.08573/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:56:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ISOGmSEyvVg6c7ZOrPu6dySTHWcjWlbR3AQQ0YOuFn1KQvy4VychD+K/3uBfYQzqn6KjW6BaOrLD45gapdLlDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:08:40.070078Z"},"content_sha256":"ff9652903cc557dd901fae79b7b9c5c85966421122d4321cb76b664f81cf3ff0","schema_version":"1.0","event_id":"sha256:ff9652903cc557dd901fae79b7b9c5c85966421122d4321cb76b664f81cf3ff0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/bundle.json","state_url":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4AKMMBRFJZECZWQBHBD2M7EVET/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":"2026-08-07T18:08:40Z","links":{"resolver":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET","bundle":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/bundle.json","state":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4AKMMBRFJZECZWQBHBD2M7EVET/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4AKMMBRFJZECZWQBHBD2M7EVET","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":"ba3d4f3931ff6c95de8a1f5f6c1c5672fe765e05016bb421b46840b0e89c3d2d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-11T17:41:53Z","title_canon_sha256":"683e8c6e7af6be93bcbcbad7a4e61d5939839d4aeca397e191e249984ff62129"},"schema_version":"1.0","source":{"id":"2412.08573","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08573","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08573v2","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08573","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"pith_short_12","alias_value":"4AKMMBRFJZEC","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"pith_short_16","alias_value":"4AKMMBRFJZECZWQB","created_at":"2026-07-05T09:56:38Z"},{"alias_kind":"pith_short_8","alias_value":"4AKMMBRF","created_at":"2026-07-05T09:56:38Z"}],"graph_snapshots":[{"event_id":"sha256:ff9652903cc557dd901fae79b7b9c5c85966421122d4321cb76b664f81cf3ff0","target":"graph","created_at":"2026-07-05T09:56:38Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.08573/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challenge of generating high-fidelity tiled garment images essential for personalized recommendations, outfit composition, and virtual try-on systems from photos of garments worn by models. Inspired by the success of Latent Diffusion Models (LDMs) in image-to-image translation, we propose a novel approach utilizing a fine-tuned StableDiffusion model. Our method features a streamlined single-stage networ","authors_text":"Ioannis Xarchakos, Theodoros Koukopoulos","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-11T17:41:53Z","title":"TryOffAnyone: Tiled Cloth Generation from a Dressed Person"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08573","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:c570536f512dd992fe65e27c1677e26e5f95bde2df483ad7362d52154f3b6e8a","target":"record","created_at":"2026-07-05T09:56:38Z","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":"ba3d4f3931ff6c95de8a1f5f6c1c5672fe765e05016bb421b46840b0e89c3d2d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-11T17:41:53Z","title_canon_sha256":"683e8c6e7af6be93bcbcbad7a4e61d5939839d4aeca397e191e249984ff62129"},"schema_version":"1.0","source":{"id":"2412.08573","kind":"arxiv","version":2}},"canonical_sha256":"e014c606254e482cda013847a67c9524f148248fd468f110ffca3613c7b8f1ec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e014c606254e482cda013847a67c9524f148248fd468f110ffca3613c7b8f1ec","first_computed_at":"2026-07-05T09:56:38.058689Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:56:38.058689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rFagxPEukn73W8dZpAmOjjaFpNOxQpe89gRO+Iv9bp8wZxuojJHAVdocu7DugM5x9mwZGMn26bZPzoX1ItjiAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:56:38.059190Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.08573","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c570536f512dd992fe65e27c1677e26e5f95bde2df483ad7362d52154f3b6e8a","sha256:ff9652903cc557dd901fae79b7b9c5c85966421122d4321cb76b664f81cf3ff0"],"state_sha256":"fc9b014981ec1c9a509c7995a3fc76fbaaf6a293b9bd0c2f45a7bb10428a31d5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M4I6CXOH/EfE6qTrqVmltxm2C9YK5Bxtfa0U4oT9E6O1i4tYv+m5flWnp3P91x7kMINViRQozGmMhMCj2dVIBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T18:08:40.082462Z","bundle_sha256":"55d12ea004e2e6f4c6acca4233fc384ab19580b9ed41fea59150ba6360b33313"}}