{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:A35T2HWWFF7QTC7IC344BJFJKD","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":"f6d30aa44b36a98c04b45ee5d1c061831cf165556b465fc3a6becfecca8bfd15","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-21T16:04:45Z","title_canon_sha256":"697a894c27b56d74c7f20df084ca82567bbcc9d2c8b5ed3dd5e43126b507c105"},"schema_version":"1.0","source":{"id":"2211.11610","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.11610","created_at":"2026-07-05T06:00:34Z"},{"alias_kind":"arxiv_version","alias_value":"2211.11610v2","created_at":"2026-07-05T06:00:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.11610","created_at":"2026-07-05T06:00:34Z"},{"alias_kind":"pith_short_12","alias_value":"A35T2HWWFF7Q","created_at":"2026-07-05T06:00:34Z"},{"alias_kind":"pith_short_16","alias_value":"A35T2HWWFF7QTC7I","created_at":"2026-07-05T06:00:34Z"},{"alias_kind":"pith_short_8","alias_value":"A35T2HWW","created_at":"2026-07-05T06:00:34Z"}],"graph_snapshots":[{"event_id":"sha256:d5dbadd130d04da8c64124005620526d78e13a790b4f54d909e09fcd6a49c388","target":"graph","created_at":"2026-07-05T06:00:34Z","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/2211.11610/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor. To tackle the accompanying memory issue, we decompose the 4D tensor hierarchically by projecting it first into three time-aware volumes and then nine compact feature planes. In this way, spatial information over time can be simultaneously captured in a compact and memory-efficient manner. When applying Tensor4D for dynamic scene reconstruction and rendering","authors_text":"Boning Liu, Hanzhang Tu, Hongwen Zhang, Ruizhi Shao, Yebin Liu, Zerong Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-21T16:04:45Z","title":"Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.11610","kind":"arxiv","version":2},"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:d7e71dd9a8a322c30d188f3fe3463e4285f6643971f6a385832030b0e80965ce","target":"record","created_at":"2026-07-05T06:00:34Z","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":"f6d30aa44b36a98c04b45ee5d1c061831cf165556b465fc3a6becfecca8bfd15","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-21T16:04:45Z","title_canon_sha256":"697a894c27b56d74c7f20df084ca82567bbcc9d2c8b5ed3dd5e43126b507c105"},"schema_version":"1.0","source":{"id":"2211.11610","kind":"arxiv","version":2}},"canonical_sha256":"06fb3d1ed6297f098be816f9c0a4a950e9d98b0f87d8aad3538753affd4eac4c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"06fb3d1ed6297f098be816f9c0a4a950e9d98b0f87d8aad3538753affd4eac4c","first_computed_at":"2026-07-05T06:00:34.877099Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:00:34.877099Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+I05HT7pPEiz26XIUUO7Wo+bzbZ84JzCJq4y/XnDwVJl+040iU3ea9Jn9h+wOZ045I7mn3aTQxJTMIDv2eAwCA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:00:34.877649Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.11610","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d7e71dd9a8a322c30d188f3fe3463e4285f6643971f6a385832030b0e80965ce","sha256:d5dbadd130d04da8c64124005620526d78e13a790b4f54d909e09fcd6a49c388"],"state_sha256":"a742db2c2e90aa64d651edb8ce1030197d2c8fba2f15e26533a03ee04aec3e1b"}