---
title: 4DWorldBench
description: "CVPR 2026 3D/4D world-generation bench: perceptual quality, Condition-4D alignment, physics, 4D consistency. WorldFoundry ships the official runtime; it does not invent a board."
---

4DWorldBench (USTC, with ZJU and BJZGCA; CVPR 2026) scores **3D/4D world generators** — Image-to-3D/4D, Video-to-4D, Text-to-3D/4D — by mapping every conditioning modality into one evaluation space. Four axes roll into `four_d_worldbench_average`:

- **Perceptual quality** — CLIP-IQA, CLIP-aesthetic, FastVQA.
- **Condition-4D alignment** — Keye-VL yes/no judges for attribute / relationship / motion / event / scene control, plus DROID-SLAM camera error (raw error is lower-better, then inverted).
- **Physical realism** — LLM-assisted physics reasoning.
- **4D consistency** — DROID-SLAM reprojection, VFIMamba interpolation, motion QA, VGG style.

Paper: [4DWorldBench](https://arxiv.org/pdf/2511.19836). Project: [yeppp27.github.io/4DWorldBench.github.io](https://yeppp27.github.io/4DWorldBench.github.io/). Code: [yeppp27/4dworldbench_code](https://github.com/yeppp27/4dworldbench_code).

WorldFoundry **hosts the official runtime** (`run_four_d_worldbench_official_runner.py`) and scores one `--dimension` at a time over a dataset JSON in the official schema. Official dataset download is not published; you supply the JSON plus generated videos.

<BenchmarkPageSections id="4dworldbench" locale="en" />

## How to run

```bash
${WORLDFOUNDRY_UNIFIED_PYTHON} \
  worldfoundry/evaluation/tasks/execution/runners/four_d_worldbench/run_four_d_worldbench_official_runner.py \
  --run-official \
  --dataset-json <dataset.json> \
  --model-name <model_name> \
  --dimension perceptual_clip_iqa_metrics \
  --generated-video-dir <generated_artifacts> \
  --output-dir tmp/4dworldbench/official \
  --json
```

Model-backed dims resolve `WORLDFOUNDRY_4DWORLDBENCH_KEYE_MODEL`, `WORLDFOUNDRY_4DWORLDBENCH_DROID_CKPT`, and `OPENAI_API_KEY`. Pixel-level dims run without them. Shared stacks: `keye_vl`, `droid_slam`, `vfimamba`, `fastvqa`. Default import path: `WORLDFOUNDRY_4DWORLDBENCH_RESULTS_PATH`.

In-tree pointers: `worldfoundry/data/benchmarks/tasks/external/4dworldbench.yaml`, `worldfoundry/evaluation/tasks/execution/runners/four_d_worldbench`.

Outputs include `scorecard.json` (primary `four_d_worldbench_average`) and `raw_metric_table.jsonl`.

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