{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UVDPKVWWYAXSZ7EOEC3NVZ3LZF","short_pith_number":"pith:UVDPKVWW","schema_version":"1.0","canonical_sha256":"a546f556d6c02f2cfc8e20b6dae76bc977d3c811b14b46bea2998bc621580646","source":{"kind":"arxiv","id":"2401.02400","version":2},"attestation_state":"computed","paper":{"title":"Learning the 3D Fauna of the Web","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea Vedaldi, Christian Rupprecht, Dor Litvak, Jiajun Wu, Ruining Li, Shangzhe Wu, Tomas Jakab, Yunzhi Zhang, Zizhang Li","submitted_at":"2024-01-04T18:32:48Z","abstract_excerpt":"Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data, which we overcome by simply learning from 2D Internet images. We show that prior category-specific attempts fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (S"},"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":"2401.02400","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-04T18:32:48Z","cross_cats_sorted":[],"title_canon_sha256":"be7339a0a739434e4108de01301a98a24bf94181ecb669fae86206f3f9fc651d","abstract_canon_sha256":"2cc240f227939662e1358b65ef77b03f2c7aa7da7ad9e69b4ba83ea0c8b665d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:49.994283Z","signature_b64":"xEFoJErlxXYz8B4mTkjNeGyJkCuV4ZdOT5sOky+Zsrj8nQ9D0KV/4mLcdcf6XUq/NNRmehQcM7hLEP+qdGEcAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a546f556d6c02f2cfc8e20b6dae76bc977d3c811b14b46bea2998bc621580646","last_reissued_at":"2026-07-05T08:02:49.993865Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:49.993865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning the 3D Fauna of the Web","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea Vedaldi, Christian Rupprecht, Dor Litvak, Jiajun Wu, Ruining Li, Shangzhe Wu, Tomas Jakab, Yunzhi Zhang, Zizhang Li","submitted_at":"2024-01-04T18:32:48Z","abstract_excerpt":"Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data, which we overcome by simply learning from 2D Internet images. We show that prior category-specific attempts fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (S"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.02400","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/2401.02400/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":"2401.02400","created_at":"2026-07-05T08:02:49.993922+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.02400v2","created_at":"2026-07-05T08:02:49.993922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.02400","created_at":"2026-07-05T08:02:49.993922+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVDPKVWWYAXS","created_at":"2026-07-05T08:02:49.993922+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVDPKVWWYAXSZ7EO","created_at":"2026-07-05T08:02:49.993922+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVDPKVWW","created_at":"2026-07-05T08:02:49.993922+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.16062","citing_title":"Advances and Trends in the 3D Reconstruction of the Shape and Motion of Animals","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF","json":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF.json","graph_json":"https://pith.science/api/pith-number/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/graph.json","events_json":"https://pith.science/api/pith-number/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/events.json","paper":"https://pith.science/paper/UVDPKVWW"},"agent_actions":{"view_html":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF","download_json":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF.json","view_paper":"https://pith.science/paper/UVDPKVWW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.02400&json=true","fetch_graph":"https://pith.science/api/pith-number/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/graph.json","fetch_events":"https://pith.science/api/pith-number/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/action/storage_attestation","attest_author":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/action/author_attestation","sign_citation":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/action/citation_signature","submit_replication":"https://pith.science/pith/UVDPKVWWYAXSZ7EOEC3NVZ3LZF/action/replication_record"}},"created_at":"2026-07-05T08:02:49.993922+00:00","updated_at":"2026-07-05T08:02:49.993922+00:00"}