OpenEnvision/WorldAtlas

OpenEnvision

Is your world model a consistent, controllable, and physically plausible scene renderer?

TL;DR — WorldAtlas evaluates the published world-model integrations on image-conditioned generation (image_static, image_dynamic, plus a closed-loop memory track at 60s and 30s) across 5 dimensions.

• [2026/10/15] 🚀 WorldAtlas is now open-sourcing as a preview! Released the dataset, evaluation code, model weights, leaderboard, and dataset gallery. 🌐

• Unified Evaluation Protocol for World Generation. We introduce WorldAtlas, the largest unified benchmark to date for video, 3D, 4D, and interactive world generation, which formulates the task as next-state generation and maps a single world specification onto each system's native interface, comparing heterogeneous models on task semantics rather than on a shared representation.
• Large-scale Diagnostic Suite for World Generation. We curate 10,000 image and video evaluation instances spanning diverse scenes, viewpoint transformations, temporal dynamics, physical interactions, and action-conditioned transitions. The suite assesses whether a system can faithfully render a world, preserve its state across viewpoints and time, simulate plausible dynamics, and make targeted changes in response to interventions, enabling capability-specific diagnosis beyond aggregate perceptual scores.
• Unified Evaluation of World Generation Paradigms. We evaluate representative video, 3D, 4D, and interactive systems under a common capability framework, revealing a tradeoff between scalable visual synthesis and persistent world state. Visual fidelity does not establish a coherent, controllable world: state must persist across views and time, evolve plausibly, and change selectively in response to interventions.

You're welcome to explore the latest results on our hosted leaderboard.

To submit your model, see the Submission guide (中文).

See the Getting Started guide for installation, data preparation, and runtime setup (中文).

Download the benchmark dataset from Hugging Face or ModelScope.

1. Complete the setup in Getting Started.
2. Generate videos and score their content-bound evaluation manifest:

# End-to-end (generate + evaluate)
worldatlas run-model \
  --config config/benchmark.yaml \
  --model-config config/models/lingbot_world/lingbot_world.yaml \
  --suites image_static

# Evaluate a completed WorldAtlas generation run
worldatlas evaluate \
  --config config/benchmark.yaml \
  --manifest artifacts/predictions/my-model/evaluation_manifest.jsonl \
  --predictions-root artifacts/predictions/my-model \
  --model-name my_custom_model

# Sharded multi-GPU evaluation
python scripts/run_eval_shards.py --help

Reports land in artifacts/benchmark/reports/<model>/ (per_sample_scores.jsonl, per_metric_summary.json, per_dimension_summary.json, leaderboard.csv).

Generation suites (what models produce and what the leaderboard scores)

Reference video corpus

These clips live under data/video and are indexed as benchmark.reference_corpus. Image-conditioned predictions are scored against that corpus.

Dimensions (headline official metrics; full list on each dimension page)

experimental is a supplemental slice and does not enter formal headline aggregates.

WorldAtlas supports several control interfaces through the same YAML adapter contract:

Steps

1. Place upstream code under thirdparty/<Name>/ (or set repo_root).
2. Download weights to ckpt/<name>/.
3. Add config/models/<family>/<name>.yaml.
4. Implement or extend an adapter in worldatlas/models/adapters/ and register it in worldatlas/models/registry.py.

Minimal local-repo template:

model:
  name: my-world-model
  family: my_world_model
  repo_root: thirdparty/MyModel
  checkpoint_dir: ckpt/my-model
  python_bin: python          # or a dedicated env interpreter
  entrypoint: infer.py
  output_ext: .mp4
  supported_suites: [image_static, image_dynamic]
  prompt_mode: prompt_target
  reference_mode: first_frame
  generation: { num_frames: 81, fps: 24 }

HTTP API template: config/models/video_api/template_video_api.yaml.example. Adapter contracts: Getting Started.

If you use WorldAtlas in your research, please cite:

@article{worldatlas2026,
  title   = {WorldAtlas: A Unified Large-Scale Benchmark for World Generation toward World Intelligence},
  author  = {OpenEnvision},
  year    = {2026},
  url     = {https://github.com/OpenEnvision/WorldAtlas}
}

This project builds upon the following excellent works:

• MEt3R — multi-view consistency
• Depth Anything 3 — geometric / photometric reconstruction
• FVMD — Fréchet video motion distance
• JEDi — video distributional similarity
• TRAJAN / BootsTAPIR — trajectory reconstruction
• MegaSAM / DROID-SLAM — camera and reprojection
• HPSv3 — human preference score
• SAM 2 / GroundingDINO — motion-region supervision
• RAFT / SEA-RAFT — optical flow
• TransNetV2 — scene-boundary detection
• AMT / VFIMamba — temporal interpolation
• … and the upstream world-model repositories listed under thirdparty/

Open an Issue or Pull Request, or email jiadonghou3@gmail.com.

• Code in this repository: MIT License
• Model weights: subject to each upstream project's license
• Benchmark media: subject to the dataset license under which you obtained the assets

WorldAtlas · Homepage · GitHub · Documentation