---
title: Physics-IQ Original
description: "DeepMind physical-understanding bench: continue a real filmed scene for 5 seconds. WorldFoundry ships the scorer; it does not invent a board."
---

Physics-IQ (Google DeepMind) asks whether a video model **continues real physics**, not whether the pixels look pretty. Each item shows a filmed scene; the model must generate the next **5 seconds**. The published protocol has **66 scenarios × 2 views** (396 items). WorldFoundry's contract tracks a **198-video single-pass** split. Scoring compares the continuation to the real future with motion-mask **spatial / spatiotemporal** agreement and an **MSE** penalty, then aggregates into `physics_iq_score` (official `final_score_orig`). The current official code also emits a component-clipped `physics_iq_stable_score`.

Paper: [Physics-IQ](https://arxiv.org/abs/2501.09038). Project: [physics-iq.github.io](https://physics-iq.github.io/). Code: [google-deepmind/physics-IQ-benchmark](https://github.com/google-deepmind/physics-IQ-benchmark).

Unlike the manipulation importers on this hub, WorldFoundry **ships the scoring runtime** in tree (`run_physics_iq_official_runner.py`, shared with `physics-iq-verified` via `--protocol`) plus prompt/scenario metadata under `worldfoundry/data/benchmarks/assets/physics-iq`. Official **media** (real videos, masks, conditioning frames) still download from DeepMind — they are not in the repo. [Physics-IQ Verified](/docs/evaluation/benchmark-hub/physics-iq-verified) uses a different dataset; the two are **not interchangeable**.

<BenchmarkPageSections id="physics-iq" locale="en" />

## How to run

Import an existing `raw_metrics.csv`:

```bash
worldfoundry-eval zoo benchmark-run \
  --benchmark-id physics-iq \
  --mode official-validation \
  --official-results-path <raw_metrics.csv> \
  --output-dir tmp/physics-iq/official-validation \
  --json
```

Recompute from 198 generated continuations named by official scenario ids:

```bash
export WORLDFOUNDRY_GENERATED_ARTIFACT_DIR=/path/to/physics-iq/generated_videos
${WORLDFOUNDRY_UNIFIED_PYTHON} \
  worldfoundry/evaluation/tasks/execution/runners/physics_iq/run_physics_iq_official_runner.py \
  --protocol physics-iq \
  --run-official \
  --dataset-root /path/to/physics-iq-benchmark \
  --generated-artifact-dir "${WORLDFOUNDRY_GENERATED_ARTIFACT_DIR}" \
  --output-dir tmp/physics-iq/official-run \
  --strict \
  --json
```

`--strict` fails on missing scenario videos. `--generated-mask-dir` can supply precomputed masks; otherwise masks come from the real videos. Outputs include `scorecard.json` (primary `physics_iq_score`), `raw_metrics.csv`, `official_metrics.json`, and per-sample rows when the source exposes them.

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