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Camera Movement v1updated 2026-09-03

World Models: Camera Movement Benchmark

Rapidatabenchmark@rapidata.ai

Abstract

This benchmark ranks world and video generation models on whether they can carry out a camera movement stated in plain terms, without referencing the scene, such as moving forward, tilting down or rolling clockwise, starting from a single still image. The rankings are updated continuously from 417,044 votes across 75,390 pairwise human-preference matches. Quality is decomposed into capability dimensions: a fixed, curated prompt set paired with a single criterion, fitted with a Bradley–Terry model on the Elo scale. All data and judgments are public.

1  Methodology

We evaluate generative model performance purely on human feedback. Humans perform inconsistently on absolute judgements, but much better in a comparative setting. Our rating is therefore based on pair-wise matches, the results of which are jointly fit using the Bradley–Terry model where the probability of model m beating model m′ is given by:

P(m ≻ m′) =eβmeβm + eβm′=11 + e−(βm − βm′)(1)

A match pairs outputs from two models for the same prompt. As evaluating all permutations is infeasible, matches are sampled, ensuring broad coverage per model. The resulting scores are scaled to the familiar Elo range via Rm = 400 βm + 1000 (2), which stretches standard Elo by ln 10 ≈ 2.3 to better highlight differences in model capability.

We curated 30 camera-movement instructions across 22 spaces and mixed and matched them into 600 prompts, held fixed to ensure reproducibility of the results.

To ensure trustworthy ratings we employ several measures, notably annotator pre-filtering via gold-standard questions, and redundancy, collecting a minimum of m responses per match (m = 5) and deciding the winner by majority. Read more

LeaderboardCompare OutputsMatchupsAnalysisPromptsDetails
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Table 1. Overall standings, Camera movement benchmark. Scores from a Bradley–Terry fit rescaled to Elo; intervals are 95% Wald intervals. Click any row to view sample outputs in Figure 1.

Methodology notesCompare Outputs
Figure 1. Sample outputs from Gemini Omni Flash 1.1 on representative prompts from the fixed set. Outputs shown are those used in matches.
Score vs. cost
75010001250$0.01$0.02$0.05$0.1$0.2cost, USD per video second (log scale)score

Figure 2. Overall Elo against the provider's list price, cost on a log scale. Teal-ringed models are the Pareto frontier, no model is both cheaper and better, joined by a step line, so picking a budget on the x-axis reads off the best model at or under it. Faded models are dominated; whiskers are the score 95% CI. Prices are hand-maintained snapshots of provider list prices; 7 models without a recorded price are not shown. Hover a model for exact numbers.

Evaluation: Models are compared on the same prompts through pairwise human judgements.
Score: Scores show relative performance within this benchmark. A higher score means a model was more likely to be preferred over the other evaluated models.
Uncertainty: The 95% confidence interval shows the uncertainty around each model’s estimated score.
Views: Results can be explored by movement match, and by prompt category.
Coverage: This benchmark includes a fixed prompt set across multiple categories, evaluated on all ranked models.
Data: Available prompts, model outputs, and evaluation results can be downloaded for further analysis.
Version: Results correspond to Camera Movement v1, last updated on 2026-11-04.

We use crowd intelligence to deliver real, diverse human feedback at scale for model evaluation and post-training.

Benchmarks
Text-to-ImageOutput Diversity (Mode Collapse)Camera MovementRelative Camera MovementPhysicsPhysicsRealismImage-to-3DSVGLLM-to-3DSuggest a model or benchmark
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