---
title: VideoScore
description: "TIGER-Lab reward model: five 1-4 aspects trained on VideoFeedback 37.6K. WorldFoundry vendors the scorer; it does not invent a board."
---

VideoScore (TIGER-Lab, University of Waterloo) is a **learned reward model**, not a prompt suite. It is trained on **VideoFeedback (37.6K human-annotated videos)** and predicts five aspect scores (official **1–4** scale) for any generated clip:

- Visual quality
- Temporal consistency
- Dynamic degree
- Text-to-video alignment
- Factual consistency

`videoscore_average` is the mean of the five. VideoScore-Bench evaluates scorer–human correlation (SPCC / pairwise). License: MIT (runtime); dataset stacks Apache-2.0 / MIT.

Paper: [VideoScore](https://arxiv.org/abs/2406.15252). Project: [tiger-ai-lab.github.io/VideoScore](https://tiger-ai-lab.github.io/VideoScore/). Code: [TIGER-AI-Lab/VideoScore](https://github.com/TIGER-AI-Lab/VideoScore). Bench data: [`TIGER-Lab/VideoScore-Bench`](https://huggingface.co/datasets/TIGER-Lab/VideoScore-Bench).

WorldFoundry **vendors the runtime**, supports bounded in-process GPU scoring, and wraps `benchmark/eval_videoscore.py`. A bounded official scorer GPU run is verified; **SPCC / pairwise full-suite parity is not**.

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

## How to run

```bash
hf download TIGER-Lab/VideoScore-Bench --repo-type dataset --local-dir /path/to/VideoScore-Bench
export WORLDFOUNDRY_VIDEOFEEDBACK_DATASET_ROOT=/path/to/VideoScore-Bench

${WORLDFOUNDRY_UNIFIED_PYTHON} \
  worldfoundry/evaluation/tasks/execution/runners/videoscore/run_videoscore_official_runner.py \
  --bench-name video_feedback \
  --bounded-sample-count 4 \
  --dataset-root "${WORLDFOUNDRY_VIDEOFEEDBACK_DATASET_ROOT}" \
  --output-dir tmp/videoscore/bounded \
  --json
```

Import upstream `eval_*_videoscore.json` via `--official-results-path` / `--from-upstream-results`. Env: `WORLDFOUNDRY_VIDEOSCORE_MODEL_DIR`, `WORLDFOUNDRY_VIDEOSCORE_FRAMES_DIR`, `WORLDFOUNDRY_VIDEOSCORE_ROOT`. Shared stack: `videoscore_reward_metric_stack`.

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

Outputs include `scorecard.json` (primary `videoscore_average`).

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