# Long Term Motion Eval

> Evaluates long-term motion representations derived from dense point-tracking against image-based baselines across five perceptual tasks. It probes temporal generalization, motion representation efficiency, and the ability to capture spatio-temporal dynamics for classification and regression. Use when the user wants to benchmark on SSV2 (Temporal Dataset subset), Jester, VB100, RAVDESS, MITFabric, ADVIO, or asks about evaluating this task. Reports classification accuracy.

- Skill: `qhjqhj00/long-term-motion-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/long-term-motion-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/long-term-motion-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/long-term-motion-eval

---


# long-term-motion-eval

> It's a Matter of Time: Three Lessons on Long-Term Motion for Perception — Davison et al. (2026) (arXiv:2602.14705, 2026)

## What this evaluates

Evaluates long-term motion representations derived from dense point-tracking against image-based baselines across five perceptual tasks. It probes temporal generalization, motion representation efficiency, and the ability to capture spatio-temporal dynamics for classification and regression.

## Datasets

- **SSV2 (Temporal Dataset subset)** — total ?; splits: unspecified (-1)
- **Jester** — total ?; splits: unspecified (-1)
- **VB100** — total ?; splits: unspecified (-1)
- **RAVDESS** — total ?; splits: unspecified (-1)
- **MITFabric** — total ?; splits: unspecified (-1)
- **ADVIO** — total ?; splits: unspecified (-1)

## Metrics

- `classification accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly predicted class labels out of the total number of instances.
- `Pearson correlation coefficient` — range: [-1, 1]
  - Measures the linear correlation between predicted and ground-truth continuous values for material properties (stiffness and area weight).
- `RMSE translational RPE` — range: other
  - Root-mean-squared error of the relative translational pose error, averaged over all time-step predictions.
- `RMSE rotational RPE` — range: other
  - Root-mean-squared error of the relative rotational pose error, averaged over all time-step predictions.

## Input / output format

**Input**: Preprocessed video clips (32, 90, 120, or 300 frames depending on the dataset) containing either pixel frames or dense point-track coordinates.

**Output**: Class labels for action, object, and emotion tasks; continuous values for material properties (stiffness, area weight); and 6-DoF pose parameters (translation and orientation) for spatial understanding.

## Scoring recipe

```python
if task in ['action', 'object', 'emotion']:
    return sum(pred == gold) / len(gold)
elif task == 'material':
    return pearsonr(pred, gold)  # per property
elif task == 'spatial':
    trans_err = sqrt(mean((pred_trans - gold_trans)**2))
    rot_err = sqrt(mean((pred_rot - gold_rot)**2))
    return trans_err, rot_err
```

## Common pitfalls

- Using the full SSV2 dataset instead of the specified 'Temporal Dataset' subset, which includes classes where temporal information is not essential.
- Ignoring dataset-specific preprocessing requirements (e.g., exact frame counts, fps normalization, clip lengths) which drastically alter motion representation quality.
- Evaluating material properties and spatial pose as classification tasks instead of regression, or using accuracy instead of Pearson correlation/RMSE.

## Evidence (verbatim from paper)

> Averaging over all time-step predictions, we then report the root-mean-squared-error (RMSE) translational RPE and RMSE rotational RPE as our final evaluation metrics.

## Citation

```bibtex
@misc{davison2026longtermmotion,
  title={It's a Matter of Time: Three Lessons on Long-Term Motion for Perception},
  author={Davison et al. (2026)},
  year={2026},
  note={arXiv:2602.14705}
}
```

- arXiv: 2602.14705