{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:R7PSN6J7MW3JPQVU2YH3NZD26X","short_pith_number":"pith:R7PSN6J7","canonical_record":{"source":{"id":"2302.14470","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-28T10:26:02Z","cross_cats_sorted":["cs.LG","physics.flu-dyn"],"title_canon_sha256":"75073aa586f26ce38b71c0377a6d53a54a7d5a491b7ca8910a24dbbbe96dafb9","abstract_canon_sha256":"cc76654c4d291f04c6447f2bf85b84d75f08a5bb8d767d9e02167e8a22e50172"},"schema_version":"1.0"},"canonical_sha256":"8fdf26f93f65b697c2b4d60fb6e47af5cf878b011d7bfbb561c69d056b13f862","source":{"kind":"arxiv","id":"2302.14470","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.14470","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"arxiv_version","alias_value":"2302.14470v1","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.14470","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"pith_short_12","alias_value":"R7PSN6J7MW3J","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"pith_short_16","alias_value":"R7PSN6J7MW3JPQVU","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"pith_short_8","alias_value":"R7PSN6J7","created_at":"2026-07-05T10:34:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:R7PSN6J7MW3JPQVU2YH3NZD26X","target":"record","payload":{"canonical_record":{"source":{"id":"2302.14470","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-28T10:26:02Z","cross_cats_sorted":["cs.LG","physics.flu-dyn"],"title_canon_sha256":"75073aa586f26ce38b71c0377a6d53a54a7d5a491b7ca8910a24dbbbe96dafb9","abstract_canon_sha256":"cc76654c4d291f04c6447f2bf85b84d75f08a5bb8d767d9e02167e8a22e50172"},"schema_version":"1.0"},"canonical_sha256":"8fdf26f93f65b697c2b4d60fb6e47af5cf878b011d7bfbb561c69d056b13f862","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:34:36.418895Z","signature_b64":"buU8UoWOLRDzypbat+NxYPoNiQ6adspcxWja/VvQn2qYCrLkEOcx5xUb6WJpPVTHssfT4PFqQ+LDaku/0H62Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fdf26f93f65b697c2b4d60fb6e47af5cf878b011d7bfbb561c69d056b13f862","last_reissued_at":"2026-07-05T10:34:36.418354Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:34:36.418354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.14470","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":"2026-07-05T10:34:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WWEUB7P/ijfEiE8VkAiYlSWJyYyF+941SDgRtNdgV3dkeXFLwkrAdObZNy/hMCx2dmT0I9eF/gvWMOKfkQN6BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:48:54.082757Z"},"content_sha256":"d8fb8d2942b1330ac535217d7c73451fd1c93b62c7fd8691684fed04940a3551","schema_version":"1.0","event_id":"sha256:d8fb8d2942b1330ac535217d7c73451fd1c93b62c7fd8691684fed04940a3551"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:R7PSN6J7MW3JPQVU2YH3NZD26X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.flu-dyn"],"primary_cat":"cs.CV","authors_text":"(2) ETH Zurich, 3), (3) TUM - Institute for Advanced Study), Aleksandra Franz (1), Barbara Solenthaler (2, Nils Thuerey (1) ((1) Technical University of Munich (TUM)","submitted_at":"2023-02-28T10:26:02Z","abstract_excerpt":"We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the complexity of this task, we show that it is possible to train the corresponding networks without requiring any 3D ground truth for training. In the absence of ground truth data we can train our model with observations from real-world capture setups instead of relying on synthetic reconstructions. We make this unsupervised training approach possible by first generating an initial prototype volume which is then moved a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.14470","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/2302.14470/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":"2026-07-05T10:34:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R5YG9bL+esJWzC6z++5SKzU8EYZ/ntZXvKbCgoHfj8Rl9MjfN0Zo///wmIvr0MjgqH1avjD/NCfLUhAoX+9zAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:48:54.083646Z"},"content_sha256":"0247ee15820d73a089b654e9a6618f0a4e0bb021d553721c69faebc837d2a5f7","schema_version":"1.0","event_id":"sha256:0247ee15820d73a089b654e9a6618f0a4e0bb021d553721c69faebc837d2a5f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R7PSN6J7MW3JPQVU2YH3NZD26X/bundle.json","state_url":"https://pith.science/pith/R7PSN6J7MW3JPQVU2YH3NZD26X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R7PSN6J7MW3JPQVU2YH3NZD26X/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":"2026-08-17T08:48:54Z","links":{"resolver":"https://pith.science/pith/R7PSN6J7MW3JPQVU2YH3NZD26X","bundle":"https://pith.science/pith/R7PSN6J7MW3JPQVU2YH3NZD26X/bundle.json","state":"https://pith.science/pith/R7PSN6J7MW3JPQVU2YH3NZD26X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R7PSN6J7MW3JPQVU2YH3NZD26X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:R7PSN6J7MW3JPQVU2YH3NZD26X","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":"cc76654c4d291f04c6447f2bf85b84d75f08a5bb8d767d9e02167e8a22e50172","cross_cats_sorted":["cs.LG","physics.flu-dyn"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-28T10:26:02Z","title_canon_sha256":"75073aa586f26ce38b71c0377a6d53a54a7d5a491b7ca8910a24dbbbe96dafb9"},"schema_version":"1.0","source":{"id":"2302.14470","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.14470","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"arxiv_version","alias_value":"2302.14470v1","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.14470","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"pith_short_12","alias_value":"R7PSN6J7MW3J","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"pith_short_16","alias_value":"R7PSN6J7MW3JPQVU","created_at":"2026-07-05T10:34:36Z"},{"alias_kind":"pith_short_8","alias_value":"R7PSN6J7","created_at":"2026-07-05T10:34:36Z"}],"graph_snapshots":[{"event_id":"sha256:0247ee15820d73a089b654e9a6618f0a4e0bb021d553721c69faebc837d2a5f7","target":"graph","created_at":"2026-07-05T10:34:36Z","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/2302.14470/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the complexity of this task, we show that it is possible to train the corresponding networks without requiring any 3D ground truth for training. In the absence of ground truth data we can train our model with observations from real-world capture setups instead of relying on synthetic reconstructions. We make this unsupervised training approach possible by first generating an initial prototype volume which is then moved a","authors_text":"(2) ETH Zurich, 3), (3) TUM - Institute for Advanced Study), Aleksandra Franz (1), Barbara Solenthaler (2, Nils Thuerey (1) ((1) Technical University of Munich (TUM)","cross_cats":["cs.LG","physics.flu-dyn"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-28T10:26:02Z","title":"Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.14470","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:d8fb8d2942b1330ac535217d7c73451fd1c93b62c7fd8691684fed04940a3551","target":"record","created_at":"2026-07-05T10:34:36Z","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":"cc76654c4d291f04c6447f2bf85b84d75f08a5bb8d767d9e02167e8a22e50172","cross_cats_sorted":["cs.LG","physics.flu-dyn"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-28T10:26:02Z","title_canon_sha256":"75073aa586f26ce38b71c0377a6d53a54a7d5a491b7ca8910a24dbbbe96dafb9"},"schema_version":"1.0","source":{"id":"2302.14470","kind":"arxiv","version":1}},"canonical_sha256":"8fdf26f93f65b697c2b4d60fb6e47af5cf878b011d7bfbb561c69d056b13f862","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8fdf26f93f65b697c2b4d60fb6e47af5cf878b011d7bfbb561c69d056b13f862","first_computed_at":"2026-07-05T10:34:36.418354Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:34:36.418354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"buU8UoWOLRDzypbat+NxYPoNiQ6adspcxWja/VvQn2qYCrLkEOcx5xUb6WJpPVTHssfT4PFqQ+LDaku/0H62Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T10:34:36.418895Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.14470","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8fb8d2942b1330ac535217d7c73451fd1c93b62c7fd8691684fed04940a3551","sha256:0247ee15820d73a089b654e9a6618f0a4e0bb021d553721c69faebc837d2a5f7"],"state_sha256":"a4227a4dad78c347e82f8a06954ef25cdc99f6df137bcbe889d976b70dee0a44"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tuc2RWleVOy0x34F4g9rWniKNHukpQV2xzJ8xV/OECqWGq7qCTYQprTn+pPsZyEGos4fj1NoaMRnOaY0/+Y1BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T08:48:54.089949Z","bundle_sha256":"f449bac172c78ef992482f1e2b09ffbac7a25e3e80dc39277fea0be3b54a3bbb"}}