{"schema":"record/v1","id":"art_01M5MDXEKRZ01DG3Q1DZVAH60Y","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What do reviews say about the limits of marker gap filling methods, including learned methods, for motion capture used in animation?","summary":"A CC BY 2025 Sensors review reports learned mocap recovery overfits benchmarks (CMU, HDM05) and degrades on long gaps and new subjects, clothing or marker sets, while classical interpolation is simple and interpretable but relies on manual input; it cites tree-based regression for long gaps. Method-level claims are secondary.","status":"active","body":{"text":"A CC BY 2025 Sensors review reports learned mocap recovery overfits benchmarks (CMU, HDM05) and degrades on long gaps and new subjects, clothing or marker sets, while classical interpolation is simple and interpretable but relies on manual input; it cites tree-based regression for long gaps. Method-level claims are secondary.\n\nCurator qualifications: Documents read 2026-10-22; no data filtered or gaps filled. GAN and joint-connectivity failure wording not re-located verbatim; cited originals not traced.\n\nLimitations: Documents read 2026-10-22; no data filtered or gaps filled.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12737201/fullTextXML","note":"Europe PMC PMC12737201 full-text XML (Sensors review, MDPI)"}]},"categories":["T07","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12737201/fullTextXML","claim":"A CC BY 2025 Sensors review reports learned mocap recovery overfits benchmarks (CMU, HDM05) and degrades on long gaps and new subjects, clothing or marker sets, while classical interpolation is simple and interpretable but relies on manual input; it cites tree-based regression for long gaps. 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Method-level claims are secondary.\n\nCurator qualifications: Documents read 2026-10-22; no data filtered or gaps filled. GAN and joint-connectivity failure wording not re-located verbatim; cited originals not traced.\n\nLimitations: Documents read 2026-10-22; no data filtered or gaps filled.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12737201/fullTextXML","note":"Europe PMC PMC12737201 full-text XML (Sensors review, MDPI)"}]},"tags":[],"type":"article","links":[],"title":"What do reviews say about the limits of marker gap filling methods, including learned methods, for motion capture used in animation?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"A CC BY 2025 Sensors review reports learned mocap recovery overfits benchmarks (CMU, HDM05) and degrades on long gaps and new subjects, clothing or marker sets, while classical interpolation is simple and interpretable but relies on manual input; it cites tree-based regression for long gaps. 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