WorldScore: A Unified Evaluation Benchmark for World Generation

Haoyi Duan, Hong-Xing Yu, Sirui Chen, Li Fei-Fei, Jiajun Wu
4/1/2025
cs.GRcs.AIcs.CV

No official code found. Best implementation: haoyi-duan/WorldScore (290 stars, MIT, last commit 2025-12-09).

Abstract

We introduce the WorldScore benchmark, the first unified benchmark for world generation. We decompose world generation into a sequence of next-scene generation tasks with explicit camera trajectory-based layout specifications, enabling unified evaluation of diverse approaches from 3D and 4D scene generation to video generation models. The WorldScore benchmark encompasses a curated dataset of 3,000 test examples that span diverse worlds: static and dynamic, indoor and outdoor, photorealistic and stylized. The WorldScore metrics evaluate generated worlds through three key aspects: controllability, quality, and dynamics. Through extensive evaluation of 19 representative models, including both open-source and closed-source ones, we reveal key insights and challenges for each category of models. Our dataset, evaluation code, and leaderboard can be found at https://haoyi-duan.github.io/WorldScore/

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Code Implementations(1)

MIT

Official implementation for WorldScore: A Unified Evaluation Benchmark for World Generation

29019Python, Jupyter NotebookMar 31, 202510 months agoMIT
benchmarkcomputer-visionworld-generation

Cite this paper

@article{duan2025worldscore,
  title  = {WorldScore: A Unified Evaluation Benchmark for World Generation},
  author = {Haoyi Duan and Hong-Xing Yu and Sirui Chen and Li Fei-Fei and Jiajun Wu},
  year   = {2025},
  eprint = {2504.00983},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.00983v2}
}

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