{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HAWU5J7QKJHDV3JVKYOPNGKZJK","short_pith_number":"pith:HAWU5J7Q","schema_version":"1.0","canonical_sha256":"382d4ea7f0524e3aed35561cf699594a91c58a29a66708ec985843cfc71fa0b9","source":{"kind":"arxiv","id":"2403.06168","version":2},"attestation_state":"computed","paper":{"title":"DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chengjie Wang, Donghao Luo, Jiangning Zhang, Jinlong Peng, Rongrong Ji, Taisong Jin, Xiaobin Hu, Xiaozhong Ji, Xu Peng, Zhengkai Jiang","submitted_at":"2024-03-10T10:39:32Z","abstract_excerpt":"Due to the difficulty and labor-consuming nature of getting highly accurate or matting annotations, there only exists a limited amount of highly accurate labels available to the public. To tackle this challenge, we propose a DiffuMatting which inherits the strong Everything generation ability of diffusion and endows the power of \"matting anything\". Our DiffuMatting can 1). act as an anything matting factory with high accurate annotations 2). be well-compatible with community LoRAs or various conditional control approaches to achieve the community-friendly art design and controllable generation"},"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":"2403.06168","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-10T10:39:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"085bcfa6e59cbff3940aebd620901cdf907c2d206c9acb637d382c0f7459ec7b","abstract_canon_sha256":"674a60fad23d763e944a909751d219d0ae872e6375055a91bd773eb19f801b74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:36.699671Z","signature_b64":"3/PlegiBDY7HC+IneshGJHhHRko82UWrqY01r14AFxOkyI0Gk8e28+2RrmN2/VZsje0Q1qzPb9jK992YTY5hCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"382d4ea7f0524e3aed35561cf699594a91c58a29a66708ec985843cfc71fa0b9","last_reissued_at":"2026-07-05T08:57:36.699086Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:36.699086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chengjie Wang, Donghao Luo, Jiangning Zhang, Jinlong Peng, Rongrong Ji, Taisong Jin, Xiaobin Hu, Xiaozhong Ji, Xu Peng, Zhengkai Jiang","submitted_at":"2024-03-10T10:39:32Z","abstract_excerpt":"Due to the difficulty and labor-consuming nature of getting highly accurate or matting annotations, there only exists a limited amount of highly accurate labels available to the public. To tackle this challenge, we propose a DiffuMatting which inherits the strong Everything generation ability of diffusion and endows the power of \"matting anything\". Our DiffuMatting can 1). act as an anything matting factory with high accurate annotations 2). be well-compatible with community LoRAs or various conditional control approaches to achieve the community-friendly art design and controllable generation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06168","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/2403.06168/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":"2403.06168","created_at":"2026-07-05T08:57:36.699150+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06168v2","created_at":"2026-07-05T08:57:36.699150+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06168","created_at":"2026-07-05T08:57:36.699150+00:00"},{"alias_kind":"pith_short_12","alias_value":"HAWU5J7QKJHD","created_at":"2026-07-05T08:57:36.699150+00:00"},{"alias_kind":"pith_short_16","alias_value":"HAWU5J7QKJHDV3JV","created_at":"2026-07-05T08:57:36.699150+00:00"},{"alias_kind":"pith_short_8","alias_value":"HAWU5J7Q","created_at":"2026-07-05T08:57:36.699150+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22360","citing_title":"Identity-Preserving Text-to-Image Generation via Dual-Level Feature Decoupling and Expert-Guided Fusion","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK","json":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK.json","graph_json":"https://pith.science/api/pith-number/HAWU5J7QKJHDV3JVKYOPNGKZJK/graph.json","events_json":"https://pith.science/api/pith-number/HAWU5J7QKJHDV3JVKYOPNGKZJK/events.json","paper":"https://pith.science/paper/HAWU5J7Q"},"agent_actions":{"view_html":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK","download_json":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK.json","view_paper":"https://pith.science/paper/HAWU5J7Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06168&json=true","fetch_graph":"https://pith.science/api/pith-number/HAWU5J7QKJHDV3JVKYOPNGKZJK/graph.json","fetch_events":"https://pith.science/api/pith-number/HAWU5J7QKJHDV3JVKYOPNGKZJK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK/action/storage_attestation","attest_author":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK/action/author_attestation","sign_citation":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK/action/citation_signature","submit_replication":"https://pith.science/pith/HAWU5J7QKJHDV3JVKYOPNGKZJK/action/replication_record"}},"created_at":"2026-07-05T08:57:36.699150+00:00","updated_at":"2026-07-05T08:57:36.699150+00:00"}