{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SCU5OSROU2VHJ4LO6KPN7P3FRD","short_pith_number":"pith:SCU5OSRO","schema_version":"1.0","canonical_sha256":"90a9d74a2ea6aa74f16ef29edfbf6588fb3417fd119950b40a9ca64b5ff263fc","source":{"kind":"arxiv","id":"1811.06229","version":1},"attestation_state":"computed","paper":{"title":"Hair-GANs: Recovering 3D Hair Structure from a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Meng Zhang, Youyi Zheng","submitted_at":"2018-11-15T08:28:00Z","abstract_excerpt":"We introduce Hair-GANs, an architecture of generative adversarial networks, to recover the 3D hair structure from a single image. The goal of our networks is to build a parametric transformation from 2D hair maps to 3D hair structure. The 3D hair structure is represented as a 3D volumetric field which encodes both the occupancy and the orientation information of the hair strands. Given a single hair image, we first align it with a bust model and extract a set of 2D maps encoding the hair orientation information in 2D, along with the bust depth map to feed into our Hair-GANs. With our generator"},"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":"1811.06229","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2018-11-15T08:28:00Z","cross_cats_sorted":[],"title_canon_sha256":"c426a347347bdeba0bf6c0846d8df79589a24ca368e330bad57b9e89dcf970db","abstract_canon_sha256":"b6b140c000905eec6f96e258f0e914f6b06697dae5c319d9038897c88429626b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:38.297553Z","signature_b64":"JU3+ipLWtJEdfj2JkbgawbIASzxEYrniBa1Eq6fB49LRrsIQNgbMh7vJW3XeZsQ7B/j0FEkwbCLEEs1l03UIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90a9d74a2ea6aa74f16ef29edfbf6588fb3417fd119950b40a9ca64b5ff263fc","last_reissued_at":"2026-05-18T00:00:38.297065Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:38.297065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hair-GANs: Recovering 3D Hair Structure from a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Meng Zhang, Youyi Zheng","submitted_at":"2018-11-15T08:28:00Z","abstract_excerpt":"We introduce Hair-GANs, an architecture of generative adversarial networks, to recover the 3D hair structure from a single image. The goal of our networks is to build a parametric transformation from 2D hair maps to 3D hair structure. The 3D hair structure is represented as a 3D volumetric field which encodes both the occupancy and the orientation information of the hair strands. Given a single hair image, we first align it with a bust model and extract a set of 2D maps encoding the hair orientation information in 2D, along with the bust depth map to feed into our Hair-GANs. With our generator"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06229","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":""},"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":"1811.06229","created_at":"2026-05-18T00:00:38.297132+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.06229v1","created_at":"2026-05-18T00:00:38.297132+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06229","created_at":"2026-05-18T00:00:38.297132+00:00"},{"alias_kind":"pith_short_12","alias_value":"SCU5OSROU2VH","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"SCU5OSROU2VHJ4LO","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"SCU5OSRO","created_at":"2026-05-18T12:32:50.500415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.12562","citing_title":"HairPort: In-context 3D-aware Hair Import and Transfer for Images","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD","json":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD.json","graph_json":"https://pith.science/api/pith-number/SCU5OSROU2VHJ4LO6KPN7P3FRD/graph.json","events_json":"https://pith.science/api/pith-number/SCU5OSROU2VHJ4LO6KPN7P3FRD/events.json","paper":"https://pith.science/paper/SCU5OSRO"},"agent_actions":{"view_html":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD","download_json":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD.json","view_paper":"https://pith.science/paper/SCU5OSRO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.06229&json=true","fetch_graph":"https://pith.science/api/pith-number/SCU5OSROU2VHJ4LO6KPN7P3FRD/graph.json","fetch_events":"https://pith.science/api/pith-number/SCU5OSROU2VHJ4LO6KPN7P3FRD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD/action/storage_attestation","attest_author":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD/action/author_attestation","sign_citation":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD/action/citation_signature","submit_replication":"https://pith.science/pith/SCU5OSROU2VHJ4LO6KPN7P3FRD/action/replication_record"}},"created_at":"2026-05-18T00:00:38.297132+00:00","updated_at":"2026-05-18T00:00:38.297132+00:00"}