{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AMGDGT4LTKZVWXE5QN7RFDTU47","short_pith_number":"pith:AMGDGT4L","schema_version":"1.0","canonical_sha256":"030c334f8b9ab35b5c9d837f128e74e7cb9e6c58d339736c85286cfe0e36e427","source":{"kind":"arxiv","id":"2411.15028","version":1},"attestation_state":"computed","paper":{"title":"FloAt: Flow Warping of Self-Attention for Clothing Animation Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Balaji Vasan Srinivasan, Duygu Ceylan, Kuldeep Kulkarni, Swasti Shreya Mishra","submitted_at":"2024-11-22T15:59:48Z","abstract_excerpt":"We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of clothing and the texture of clothing like leopard, striped, or plain, and a sequence of normal maps that capture the underlying animation that we desire in the output. The backbone of our method is a normal-map conditioned ControlNet which is operated in a training-free regime. The key observation is that the underlying animation is embedded in the "},"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":"2411.15028","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-22T15:59:48Z","cross_cats_sorted":[],"title_canon_sha256":"ca82a17641eadc8e2f0da5a5692c9afcf89626bdb40fda27ea67e01dcc7a6d0c","abstract_canon_sha256":"962057dd2fd8b1fa500ad567402eb48d30639d5f77ec4884f7a0a7acd9160e60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:15.276648Z","signature_b64":"tMxm2Xy5bBSoKGnZGbEvEf1k9IcNMzmU+Z58PGA2Aod/ADyl9jioySpEIVuVKqdCaMpYOe2JWqbX8grmSFRYCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"030c334f8b9ab35b5c9d837f128e74e7cb9e6c58d339736c85286cfe0e36e427","last_reissued_at":"2026-07-05T09:39:15.276207Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:15.276207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FloAt: Flow Warping of Self-Attention for Clothing Animation Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Balaji Vasan Srinivasan, Duygu Ceylan, Kuldeep Kulkarni, Swasti Shreya Mishra","submitted_at":"2024-11-22T15:59:48Z","abstract_excerpt":"We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of clothing and the texture of clothing like leopard, striped, or plain, and a sequence of normal maps that capture the underlying animation that we desire in the output. The backbone of our method is a normal-map conditioned ControlNet which is operated in a training-free regime. The key observation is that the underlying animation is embedded in the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15028","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/2411.15028/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":"2411.15028","created_at":"2026-07-05T09:39:15.276264+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15028v1","created_at":"2026-07-05T09:39:15.276264+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15028","created_at":"2026-07-05T09:39:15.276264+00:00"},{"alias_kind":"pith_short_12","alias_value":"AMGDGT4LTKZV","created_at":"2026-07-05T09:39:15.276264+00:00"},{"alias_kind":"pith_short_16","alias_value":"AMGDGT4LTKZVWXE5","created_at":"2026-07-05T09:39:15.276264+00:00"},{"alias_kind":"pith_short_8","alias_value":"AMGDGT4L","created_at":"2026-07-05T09:39:15.276264+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/AMGDGT4LTKZVWXE5QN7RFDTU47","json":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47.json","graph_json":"https://pith.science/api/pith-number/AMGDGT4LTKZVWXE5QN7RFDTU47/graph.json","events_json":"https://pith.science/api/pith-number/AMGDGT4LTKZVWXE5QN7RFDTU47/events.json","paper":"https://pith.science/paper/AMGDGT4L"},"agent_actions":{"view_html":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47","download_json":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47.json","view_paper":"https://pith.science/paper/AMGDGT4L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15028&json=true","fetch_graph":"https://pith.science/api/pith-number/AMGDGT4LTKZVWXE5QN7RFDTU47/graph.json","fetch_events":"https://pith.science/api/pith-number/AMGDGT4LTKZVWXE5QN7RFDTU47/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47/action/storage_attestation","attest_author":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47/action/author_attestation","sign_citation":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47/action/citation_signature","submit_replication":"https://pith.science/pith/AMGDGT4LTKZVWXE5QN7RFDTU47/action/replication_record"}},"created_at":"2026-07-05T09:39:15.276264+00:00","updated_at":"2026-07-05T09:39:15.276264+00:00"}