{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:FHTPEN5S6TX5YQWFVXZYM4I2YO","short_pith_number":"pith:FHTPEN5S","canonical_record":{"source":{"id":"1502.00192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-01T04:09:23Z","cross_cats_sorted":[],"title_canon_sha256":"543defa808b0b00d2cecb407f1e8cef50270a89cebdb1e0b538deef677f5d8d2","abstract_canon_sha256":"a3b57d40afeb52c935849a83a69abb1b0ff2cecf11ea9d6e242662c4c8fc5883"},"schema_version":"1.0"},"canonical_sha256":"29e6f237b2f4efdc42c5adf386711ac3999523aa9283da5a7b0beebb8f632de2","source":{"kind":"arxiv","id":"1502.00192","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.00192","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"arxiv_version","alias_value":"1502.00192v1","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.00192","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"pith_short_12","alias_value":"FHTPEN5S6TX5","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"FHTPEN5S6TX5YQWF","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"FHTPEN5S","created_at":"2026-05-18T12:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:FHTPEN5S6TX5YQWFVXZYM4I2YO","target":"record","payload":{"canonical_record":{"source":{"id":"1502.00192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-01T04:09:23Z","cross_cats_sorted":[],"title_canon_sha256":"543defa808b0b00d2cecb407f1e8cef50270a89cebdb1e0b538deef677f5d8d2","abstract_canon_sha256":"a3b57d40afeb52c935849a83a69abb1b0ff2cecf11ea9d6e242662c4c8fc5883"},"schema_version":"1.0"},"canonical_sha256":"29e6f237b2f4efdc42c5adf386711ac3999523aa9283da5a7b0beebb8f632de2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:28:06.511704Z","signature_b64":"6woTFX66VKm7I1bckxMuKyLGkaez3UAzRnBOKbzVntobG14GooMsUiuK6wIz6qjFCCnIky5Q2AhTiTI/oAtNBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29e6f237b2f4efdc42c5adf386711ac3999523aa9283da5a7b0beebb8f632de2","last_reissued_at":"2026-05-18T02:28:06.511328Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:28:06.511328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.00192","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":"2026-05-18T02:28:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KqCaauPpoennNcBiGF4ZdNwdZWzxmd+dLuEFzEFvLeONlT+JwdynfHS1GgLOxvUhHXHBJuS8eU1R8O05oByhDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T21:43:19.774900Z"},"content_sha256":"57fc630c9c61afb800b9676db069d7606142ecfa12ba4307e4f91cba8d02a42c","schema_version":"1.0","event_id":"sha256:57fc630c9c61afb800b9676db069d7606142ecfa12ba4307e4f91cba8d02a42c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:FHTPEN5S6TX5YQWFVXZYM4I2YO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pose and Shape Estimation with Discriminatively Learned Parts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kostas Daniilidis, Menglong Zhu, Xiaowei Zhou","submitted_at":"2015-02-01T04:09:23Z","abstract_excerpt":"We introduce a new approach for estimating the 3D pose and the 3D shape of an object from a single image. Given a training set of view exemplars, we learn and select appearance-based discriminative parts which are mapped onto the 3D model from the training set through a facil- ity location optimization. The training set of 3D models is summarized into a sparse set of shapes from which we can generalize by linear combination. Given a test picture, we detect hypotheses for each part. The main challenge is to select from these hypotheses and compute the 3D pose and shape coefficients at the same "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.00192","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:28:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cn0fFW91937JBtSV+gL+0pd3dLggwysAfA45Il61W1S2TFg+Yq9lMxBus1wtmLqPDhxMswTl62LnSW4q75B4DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T21:43:19.775996Z"},"content_sha256":"7eaae15119a7ab7c96c099a101d243415032ce97c4695a3948a50ad25cb5af5d","schema_version":"1.0","event_id":"sha256:7eaae15119a7ab7c96c099a101d243415032ce97c4695a3948a50ad25cb5af5d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FHTPEN5S6TX5YQWFVXZYM4I2YO/bundle.json","state_url":"https://pith.science/pith/FHTPEN5S6TX5YQWFVXZYM4I2YO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FHTPEN5S6TX5YQWFVXZYM4I2YO/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":"2026-05-26T21:43:19Z","links":{"resolver":"https://pith.science/pith/FHTPEN5S6TX5YQWFVXZYM4I2YO","bundle":"https://pith.science/pith/FHTPEN5S6TX5YQWFVXZYM4I2YO/bundle.json","state":"https://pith.science/pith/FHTPEN5S6TX5YQWFVXZYM4I2YO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FHTPEN5S6TX5YQWFVXZYM4I2YO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:FHTPEN5S6TX5YQWFVXZYM4I2YO","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":"a3b57d40afeb52c935849a83a69abb1b0ff2cecf11ea9d6e242662c4c8fc5883","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-01T04:09:23Z","title_canon_sha256":"543defa808b0b00d2cecb407f1e8cef50270a89cebdb1e0b538deef677f5d8d2"},"schema_version":"1.0","source":{"id":"1502.00192","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.00192","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"arxiv_version","alias_value":"1502.00192v1","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.00192","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"pith_short_12","alias_value":"FHTPEN5S6TX5","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"FHTPEN5S6TX5YQWF","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"FHTPEN5S","created_at":"2026-05-18T12:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:7eaae15119a7ab7c96c099a101d243415032ce97c4695a3948a50ad25cb5af5d","target":"graph","created_at":"2026-05-18T02:28:06Z","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"},"paper":{"abstract_excerpt":"We introduce a new approach for estimating the 3D pose and the 3D shape of an object from a single image. Given a training set of view exemplars, we learn and select appearance-based discriminative parts which are mapped onto the 3D model from the training set through a facil- ity location optimization. The training set of 3D models is summarized into a sparse set of shapes from which we can generalize by linear combination. Given a test picture, we detect hypotheses for each part. The main challenge is to select from these hypotheses and compute the 3D pose and shape coefficients at the same ","authors_text":"Kostas Daniilidis, Menglong Zhu, Xiaowei Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-01T04:09:23Z","title":"Pose and Shape Estimation with Discriminatively Learned Parts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.00192","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":"sha256:57fc630c9c61afb800b9676db069d7606142ecfa12ba4307e4f91cba8d02a42c","target":"record","created_at":"2026-05-18T02:28:06Z","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":"a3b57d40afeb52c935849a83a69abb1b0ff2cecf11ea9d6e242662c4c8fc5883","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-01T04:09:23Z","title_canon_sha256":"543defa808b0b00d2cecb407f1e8cef50270a89cebdb1e0b538deef677f5d8d2"},"schema_version":"1.0","source":{"id":"1502.00192","kind":"arxiv","version":1}},"canonical_sha256":"29e6f237b2f4efdc42c5adf386711ac3999523aa9283da5a7b0beebb8f632de2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"29e6f237b2f4efdc42c5adf386711ac3999523aa9283da5a7b0beebb8f632de2","first_computed_at":"2026-05-18T02:28:06.511328Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:28:06.511328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6woTFX66VKm7I1bckxMuKyLGkaez3UAzRnBOKbzVntobG14GooMsUiuK6wIz6qjFCCnIky5Q2AhTiTI/oAtNBg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:28:06.511704Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.00192","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:57fc630c9c61afb800b9676db069d7606142ecfa12ba4307e4f91cba8d02a42c","sha256:7eaae15119a7ab7c96c099a101d243415032ce97c4695a3948a50ad25cb5af5d"],"state_sha256":"c6874ad9ccd6386d2f1b9c5a8875a52559cdbd1070c0336cf33909b304274309"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gHJOaemUO6L2moIl1zH5iOAKfsN53e8QqGjCUyYa0+hq2dAo1Fm/XxCyu7F42TxTs2FN9iKA2KND+EZlEgVnAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T21:43:19.778907Z","bundle_sha256":"8c8ce6af11f459f9990ecfcf2f9d4d9b63339d2eed990b32d499ce58953968c3"}}