{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:K3C4JHUUNYL3UK5GO5ETHBX6VX","short_pith_number":"pith:K3C4JHUU","schema_version":"1.0","canonical_sha256":"56c5c49e946e17ba2ba677493386feade90b31c6204a9788fc84a43d6aa24339","source":{"kind":"arxiv","id":"2207.07684","version":1},"attestation_state":"computed","paper":{"title":"Node Graph Optimization Using Differentiable Proxies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Holly Rushmeier, Milo\\v{s} Ha\\v{s}an, Paul Guerrero, Valentin Deschaintre, Yiwei Hu","submitted_at":"2022-07-15T18:05:46Z","abstract_excerpt":"Graph-based procedural materials are ubiquitous in content production industries. Procedural models allow the creation of photorealistic materials with parametric control for flexible editing of appearance. However, designing a specific material is a time-consuming process in terms of building a model and fine-tuning parameters. Previous work [Hu et al. 2022; Shi et al. 2020] introduced material graph optimization frameworks for matching target material samples. However, these previous methods were limited to optimizing differentiable functions in the graphs. In this paper, we propose a fully "},"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":"2207.07684","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2022-07-15T18:05:46Z","cross_cats_sorted":[],"title_canon_sha256":"0e05309330cfa0437e4a54cfcc31605994147da29bc0ffa79dbc0e769ee50734","abstract_canon_sha256":"cec0a220f6a8c2505779233f8ae9e212209219f8b1db517f68d2a503b7540f93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:40:35.780273Z","signature_b64":"aSHKbQ2Au1+bZvFulsX2R0aoYhtpytKNrxI8Gk0xhNxkXMIfpT+IIOIYJV8gsp+MefKBbp9156P2CcYMUrrlAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56c5c49e946e17ba2ba677493386feade90b31c6204a9788fc84a43d6aa24339","last_reissued_at":"2026-07-05T04:40:35.779737Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:40:35.779737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Node Graph Optimization Using Differentiable Proxies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Holly Rushmeier, Milo\\v{s} Ha\\v{s}an, Paul Guerrero, Valentin Deschaintre, Yiwei Hu","submitted_at":"2022-07-15T18:05:46Z","abstract_excerpt":"Graph-based procedural materials are ubiquitous in content production industries. Procedural models allow the creation of photorealistic materials with parametric control for flexible editing of appearance. However, designing a specific material is a time-consuming process in terms of building a model and fine-tuning parameters. Previous work [Hu et al. 2022; Shi et al. 2020] introduced material graph optimization frameworks for matching target material samples. However, these previous methods were limited to optimizing differentiable functions in the graphs. In this paper, we propose a fully "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.07684","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/2207.07684/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":"2207.07684","created_at":"2026-07-05T04:40:35.779798+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.07684v1","created_at":"2026-07-05T04:40:35.779798+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.07684","created_at":"2026-07-05T04:40:35.779798+00:00"},{"alias_kind":"pith_short_12","alias_value":"K3C4JHUUNYL3","created_at":"2026-07-05T04:40:35.779798+00:00"},{"alias_kind":"pith_short_16","alias_value":"K3C4JHUUNYL3UK5G","created_at":"2026-07-05T04:40:35.779798+00:00"},{"alias_kind":"pith_short_8","alias_value":"K3C4JHUU","created_at":"2026-07-05T04:40:35.779798+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/K3C4JHUUNYL3UK5GO5ETHBX6VX","json":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX.json","graph_json":"https://pith.science/api/pith-number/K3C4JHUUNYL3UK5GO5ETHBX6VX/graph.json","events_json":"https://pith.science/api/pith-number/K3C4JHUUNYL3UK5GO5ETHBX6VX/events.json","paper":"https://pith.science/paper/K3C4JHUU"},"agent_actions":{"view_html":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX","download_json":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX.json","view_paper":"https://pith.science/paper/K3C4JHUU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.07684&json=true","fetch_graph":"https://pith.science/api/pith-number/K3C4JHUUNYL3UK5GO5ETHBX6VX/graph.json","fetch_events":"https://pith.science/api/pith-number/K3C4JHUUNYL3UK5GO5ETHBX6VX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX/action/storage_attestation","attest_author":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX/action/author_attestation","sign_citation":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX/action/citation_signature","submit_replication":"https://pith.science/pith/K3C4JHUUNYL3UK5GO5ETHBX6VX/action/replication_record"}},"created_at":"2026-07-05T04:40:35.779798+00:00","updated_at":"2026-07-05T04:40:35.779798+00:00"}