{"paper":{"title":"Graphical X Splatting (GraphiXS): A Graphical Model for 4D Gaussian Splatting under Uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"GraphiXS introduces a graphical probabilistic model to incorporate data uncertainty into 4D Gaussian Splatting.","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Deshan Gong, Do\\u{g}a Y{\\i}lmaz, He Wang, Jialin Zhu","submitted_at":"2026-01-27T17:50:07Z","abstract_excerpt":"We propose a new framework to systematically incorporate data uncertainty in Gaussian Splatting. Being the new paradigm of neural rendering, Gaussian Splatting has been investigated in many applications, with the main effort in extending its representation, improving its optimization process, and accelerating its speed. However, one orthogonal, much needed, but under-explored area is data uncertainty. In standard 4D Gaussian Splatting, data uncertainty can manifest as view sparsity, missing frames, camera asynchronization, etc. So far, there has been little research to holistically incorporati"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"GraphiXS is a new probabilistic framework that considers multiple types of data uncertainty, aiming for a fundamental augmentation of the current 4D Gaussian Splatting paradigm into a probabilistic setting.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That various types of data uncertainty (view sparsity, missing frames, camera asynchronization) can be holistically incorporated under a single graphical model framework that can be instantiated with primitives such as Gaussians or Student's-t.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"GraphiXS is a new probabilistic graphical framework that augments 4D Gaussian Splatting to systematically handle multiple types of data uncertainty such as view sparsity and missing frames.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"GraphiXS introduces a graphical probabilistic model to incorporate data uncertainty into 4D Gaussian Splatting.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"56957fb51ffd6765bcfea031a041ee7e0a8ca775cd9ead99b9b3a77ec3d6dc2f"},"source":{"id":"2601.19843","kind":"arxiv","version":3},"verdict":{"id":"6fe9068e-b127-427f-99cc-03e2d2cc51f3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T10:33:04.615082Z","strongest_claim":"GraphiXS is a new probabilistic framework that considers multiple types of data uncertainty, aiming for a fundamental augmentation of the current 4D Gaussian Splatting paradigm into a probabilistic setting.","one_line_summary":"GraphiXS is a new probabilistic graphical framework that augments 4D Gaussian Splatting to systematically handle multiple types of data uncertainty such as view sparsity and missing frames.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That various types of data uncertainty (view sparsity, missing frames, camera asynchronization) can be holistically incorporated under a single graphical model framework that can be instantiated with primitives such as Gaussians or Student's-t.","pith_extraction_headline":"GraphiXS introduces a graphical probabilistic model to incorporate data uncertainty into 4D Gaussian Splatting."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.19843/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"}