---
title: T2VWorldBench
description: "WACV 2026 world-knowledge T2V bench: 1,200 prompts over six categories. WorldFoundry imports official rows; VLM judging stays upstream."
---

T2VWorldBench (San Jose State, with UC Berkeley, UW-Madison, UW, and GUET; WACV 2026) asks whether text-to-video models can use **world knowledge** for factual generation. Official size: **6 categories, 60 subcategories, 1,200 prompts** — physics, nature, activity, culture, causality, object knowledge. Scoring mixes human evaluation and VLM judging. The paper's 10-system study found most models fail at knowledge-correct video.

The released `eval.py` writes `quality`, `realism`, `relevance`, and `consistency` scores on 0–5 scales plus a `final` score on a 0–20 scale. The six knowledge categories describe the prompt suite; the released CSV does not contain separate category scores.

Paper: [T2VWorldBench](https://arxiv.org/abs/2507.18107). Code: [magiclinux/world_knowledge](https://github.com/magiclinux/world_knowledge). Prompt CSV is vendored; full metadata / sample videos are Drive-hosted.

WorldFoundry **vendors runtime source and normalizes official result files**. Full official VLM evaluation still needs the upstream LLaVA runtime and staged Drive assets.

<BenchmarkPageSections id="t2vworldbench" locale="en" />

## How to run

```bash
worldfoundry-eval zoo benchmark-run \
  --benchmark-id t2vworldbench \
  --mode official-validation \
  --official-results-path <model_video_assessment_scores.csv> \
  --generated-artifact-dir <generated_videos> \
  --output-dir tmp/t2vworldbench/official-validation \
  --json
```

Default import: `WORLDFOUNDRY_T2VWORLDBENCH_RESULTS_PATH`. Optional root: `WORLDFOUNDRY_T2VWORLDBENCH_ROOT`. In-tree prompt: `worldfoundry/data/benchmarks/assets/t2vworldbench/prompt/prompt.csv`.

In-tree pointers: `worldfoundry/data/benchmarks/tasks/external/t2vworldbench.yaml`, `worldfoundry/evaluation/tasks/execution/runners/t2vworldbench/runtime/t2vworldbench`.

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

[← Benchmark Hub](/docs/evaluation/benchmark-hub)