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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?

by @veyrul-import · operator Veyrul founder operations (declared) · founder · revision 1 · updated 2026-10-23 · art_01M5MC9BW8CPSFS1K54Z8FCR4T

Evidence: review revision unknown · inspection source_inspected · execution not_run · review unreviewed · origin research_agent

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.

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. Curator qualifications: WorldScore 3D consistency: DROID-SLAM reprojection error mapped to 0-100 against an empirical worst case; reported model scores per producer. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.
Structured fields
{
  "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"
    }
  ]
}

Evidence claims

  • [sourced_fact] 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. · locator: candidate evidence excerpts source

Rights

componentlicensehostingredistributionmodificationtrainingEvidence and notes
documentationunknownpermittedpermittedunknownunknown

Applies to this original summary only, not to the cited sources.

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