{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:GEEYYXI62AGHXCNRGEXRSQWMUQ","short_pith_number":"pith:GEEYYXI6","canonical_record":{"source":{"id":"2008.08474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-19T14:26:47Z","cross_cats_sorted":[],"title_canon_sha256":"1acecc7aec2448aea891bd03ad051ae6a9b7847e16065d1a2f56385384fb8749","abstract_canon_sha256":"37eb726755efe5b360f0882567c6173a2e0549f48e0da673ffbe32811a08e76b"},"schema_version":"1.0"},"canonical_sha256":"31098c5d1ed00c7b89b1312f1942cca427c9196afde68f0801d1bb79e7dd8280","source":{"kind":"arxiv","id":"2008.08474","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.08474","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"arxiv_version","alias_value":"2008.08474v1","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.08474","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"pith_short_12","alias_value":"GEEYYXI62AGH","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"pith_short_16","alias_value":"GEEYYXI62AGHXCNR","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"pith_short_8","alias_value":"GEEYYXI6","created_at":"2026-07-05T01:28:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:GEEYYXI62AGHXCNRGEXRSQWMUQ","target":"record","payload":{"canonical_record":{"source":{"id":"2008.08474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-19T14:26:47Z","cross_cats_sorted":[],"title_canon_sha256":"1acecc7aec2448aea891bd03ad051ae6a9b7847e16065d1a2f56385384fb8749","abstract_canon_sha256":"37eb726755efe5b360f0882567c6173a2e0549f48e0da673ffbe32811a08e76b"},"schema_version":"1.0"},"canonical_sha256":"31098c5d1ed00c7b89b1312f1942cca427c9196afde68f0801d1bb79e7dd8280","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:28:26.131891Z","signature_b64":"SnkGnUi/Q7vhIwIE5naX2qBaKN9LWtwXP5MDrF/dBYozW2OsfTmtTeiqUyeNN7uN4xCW719k32Dq7pUwpXOqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31098c5d1ed00c7b89b1312f1942cca427c9196afde68f0801d1bb79e7dd8280","last_reissued_at":"2026-07-05T01:28:26.131476Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:28:26.131476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2008.08474","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:28:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J2W/Cwu/7dyCw6i1gt6qpZGcAqK2Jh9CpZFFAJalqfQpBtNG8yaaTxezstiQ82O6iKgiMhh6P9MZoT278UY2CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:29:30.990673Z"},"content_sha256":"070894b8a6fef5ea69d59637ba30309caeca5c96561c15be3cf996321fe8dfda","schema_version":"1.0","event_id":"sha256:070894b8a6fef5ea69d59637ba30309caeca5c96561c15be3cf996321fe8dfda"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:GEEYYXI62AGHXCNRGEXRSQWMUQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Human Body Model Fitting by Learned Gradient Descent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jie Song, Otmar Hilliges, Xu Chen","submitted_at":"2020-08-19T14:26:47Z","abstract_excerpt":"We propose a novel algorithm for the fitting of 3D human shape to images. Combining the accuracy and refinement capabilities of iterative gradient-based optimization techniques with the robustness of deep neural networks, we propose a gradient descent algorithm that leverages a neural network to predict the parameter update rule for each iteration. This per-parameter and state-aware update guides the optimizer towards a good solution in very few steps, converging in typically few steps. During training our approach only requires MoCap data of human poses, parametrized via SMPL. From this data "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.08474","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/2008.08474/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:28:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4w8hyXhlSN86CRx/KdBBOcLcHT9SB5JbwjMC7TUiDjVyAX2o72jVF2KLvtCyu84lxc5bi2JxRrKQnYB1PdjwAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:29:30.991172Z"},"content_sha256":"7100303e12a130e8f01755681a2182a02a86fcb03f0a6249e1985962655e37fe","schema_version":"1.0","event_id":"sha256:7100303e12a130e8f01755681a2182a02a86fcb03f0a6249e1985962655e37fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GEEYYXI62AGHXCNRGEXRSQWMUQ/bundle.json","state_url":"https://pith.science/pith/GEEYYXI62AGHXCNRGEXRSQWMUQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GEEYYXI62AGHXCNRGEXRSQWMUQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T18:29:30Z","links":{"resolver":"https://pith.science/pith/GEEYYXI62AGHXCNRGEXRSQWMUQ","bundle":"https://pith.science/pith/GEEYYXI62AGHXCNRGEXRSQWMUQ/bundle.json","state":"https://pith.science/pith/GEEYYXI62AGHXCNRGEXRSQWMUQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GEEYYXI62AGHXCNRGEXRSQWMUQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:GEEYYXI62AGHXCNRGEXRSQWMUQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"37eb726755efe5b360f0882567c6173a2e0549f48e0da673ffbe32811a08e76b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-19T14:26:47Z","title_canon_sha256":"1acecc7aec2448aea891bd03ad051ae6a9b7847e16065d1a2f56385384fb8749"},"schema_version":"1.0","source":{"id":"2008.08474","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.08474","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"arxiv_version","alias_value":"2008.08474v1","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.08474","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"pith_short_12","alias_value":"GEEYYXI62AGH","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"pith_short_16","alias_value":"GEEYYXI62AGHXCNR","created_at":"2026-07-05T01:28:26Z"},{"alias_kind":"pith_short_8","alias_value":"GEEYYXI6","created_at":"2026-07-05T01:28:26Z"}],"graph_snapshots":[{"event_id":"sha256:7100303e12a130e8f01755681a2182a02a86fcb03f0a6249e1985962655e37fe","target":"graph","created_at":"2026-07-05T01:28:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2008.08474/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a novel algorithm for the fitting of 3D human shape to images. Combining the accuracy and refinement capabilities of iterative gradient-based optimization techniques with the robustness of deep neural networks, we propose a gradient descent algorithm that leverages a neural network to predict the parameter update rule for each iteration. This per-parameter and state-aware update guides the optimizer towards a good solution in very few steps, converging in typically few steps. During training our approach only requires MoCap data of human poses, parametrized via SMPL. From this data ","authors_text":"Jie Song, Otmar Hilliges, Xu Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-19T14:26:47Z","title":"Human Body Model Fitting by Learned Gradient Descent"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.08474","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:070894b8a6fef5ea69d59637ba30309caeca5c96561c15be3cf996321fe8dfda","target":"record","created_at":"2026-07-05T01:28:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"37eb726755efe5b360f0882567c6173a2e0549f48e0da673ffbe32811a08e76b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-19T14:26:47Z","title_canon_sha256":"1acecc7aec2448aea891bd03ad051ae6a9b7847e16065d1a2f56385384fb8749"},"schema_version":"1.0","source":{"id":"2008.08474","kind":"arxiv","version":1}},"canonical_sha256":"31098c5d1ed00c7b89b1312f1942cca427c9196afde68f0801d1bb79e7dd8280","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31098c5d1ed00c7b89b1312f1942cca427c9196afde68f0801d1bb79e7dd8280","first_computed_at":"2026-07-05T01:28:26.131476Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:28:26.131476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SnkGnUi/Q7vhIwIE5naX2qBaKN9LWtwXP5MDrF/dBYozW2OsfTmtTeiqUyeNN7uN4xCW719k32Dq7pUwpXOqDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:28:26.131891Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.08474","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:070894b8a6fef5ea69d59637ba30309caeca5c96561c15be3cf996321fe8dfda","sha256:7100303e12a130e8f01755681a2182a02a86fcb03f0a6249e1985962655e37fe"],"state_sha256":"7a510ea8591f166873854e7fb75b864c17f5a042cd599a76164b718060c07253"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SxWhfw0IdGLMhaCo6N8CcTo2DvlIDMDe+kO7s7eDK9ausz3bZtFRtRnY7a48HUwApwmqCQ0QP3u/V0jpFCYGBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:29:30.995966Z","bundle_sha256":"d710525e301947a61a6ae9fdaea0665dff89532532060c3e646aa87929d20b0a"}}