{"schema":"record/v1","id":"art_01M5MC9BW8CPSFS1K54Z8FCR4T","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What does WorldScore's DROID-SLAM reprojection-error '3D Consistency' score measure and miss, what do named closed and open video models score on it, and how much has it been validated?","summary":"WorldScore's 3D Consistency runs DROID-SLAM on a generated static-world clip and averages the reprojection error of co-visible pixels between consecutive frames, then maps it to 0-100 against an empirical worst case (a frame-interpolated clip between unrelated images). Reported scores: Gen-3 68.31, Hailuo 67.18, DynamiCrafter 72.90, CogVideoX-I2V 86.21, 3D scene generators LucidDreamer 90.37 and WonderWorld 86.87. Its authors check it with human pairwise tests (91.7% and 97.3% agreement for 30-point bucket gaps); an independent injected-warp test found it good on scene-level but weak on object-level deformation. In code it assumes a fixed lens and cannot score dynamic subjects.","status":"active","body":{"text":"WorldScore's 3D Consistency runs DROID-SLAM on a generated static-world clip and averages the reprojection error of co-visible pixels between consecutive frames, then maps it to 0-100 against an empirical worst case (a frame-interpolated clip between unrelated images). Reported scores: Gen-3 68.31, Hailuo 67.18, DynamiCrafter 72.90, CogVideoX-I2V 86.21, 3D scene generators LucidDreamer 90.37 and WonderWorld 86.87. Its authors check it with human pairwise tests (91.7% and 97.3% agreement for 30-point bucket gaps); an independent injected-warp test found it good on scene-level but weak on object-level deformation. In code it assumes a fixed lens and cannot score dynamic subjects.\n\nCurator qualifications: WorldScore 3D consistency: DROID-SLAM reprojection error mapped to 0-100 against an empirical worst case; reported model scores per producer.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2504.00983v2","note":"candidate evidence excerpts"},{"url":"https://github.com/haoyi-duan/WorldScore/blob/096c75fc4ada9c7d92e03140c3c1f0c8f383b61f/worldscore/benchmark/metrics/third_party/reprojection_error_metrics.py","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2512.22274v5","note":"candidate evidence excerpts"}]},"categories":["T06","T15","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://arxiv.org/html/2504.00983v2","claim":"WorldScore's 3D Consistency runs DROID-SLAM on a generated static-world clip and averages the reprojection error of co-visible pixels between consecutive frames, then maps it to 0-100 against an empirical worst case (a frame-interpolated clip between unrelated images). Reported scores: Gen-3 68.31, Hailuo 67.18, DynamiCrafter 72.90, CogVideoX-I2V 86.21, 3D scene generators LucidDreamer 90.37 and WonderWorld 86.87. Its authors check it with human pairwise tests (91.7% and 97.3% agreement for 30-point bucket gaps); an independent injected-warp test found it good on scene-level but weak on object-level deformation. 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Reported scores: Gen-3 68.31, Hailuo 67.18, DynamiCrafter 72.90, CogVideoX-I2V 86.21, 3D scene generators LucidDreamer 90.37 and WonderWorld 86.87. Its authors check it with human pairwise tests (91.7% and 97.3% agreement for 30-point bucket gaps); an independent injected-warp test found it good on scene-level but weak on object-level deformation. In code it assumes a fixed lens and cannot score dynamic subjects.\n\nCurator qualifications: WorldScore 3D consistency: DROID-SLAM reprojection error mapped to 0-100 against an empirical worst case; reported model scores per producer.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2504.00983v2","note":"candidate evidence excerpts"},{"url":"https://github.com/haoyi-duan/WorldScore/blob/096c75fc4ada9c7d92e03140c3c1f0c8f383b61f/worldscore/benchmark/metrics/third_party/reprojection_error_metrics.py","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2512.22274v5","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"What does WorldScore's DROID-SLAM reprojection-error '3D Consistency' score measure and miss, what do named closed and open video models score on it, and how much has it been validated?","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":"WorldScore's 3D Consistency runs DROID-SLAM on a generated static-world clip and averages the reprojection error of co-visible pixels between consecutive frames, then maps it to 0-100 against an empirical worst case (a frame-interpolated clip between unrelated images). 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