# Humanscore Eval

> Evaluates the biomechanical plausibility and realism of human motion in AI-generated videos by measuring anatomical, kinematic, and kinetic correctness. It also assesses how well these automated metrics correlate with human preference judgments. Use when the user wants to benchmark on HumanScore Benchmark, or asks about evaluating this task. Reports Kinetic Correctness.

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

---


# humanscore-eval

> HumanScore: Benchmarking Human Motions in Generated Videos — Fang et al. (2026) (arXiv:2604.20157, 2026)

## What this evaluates

Evaluates the biomechanical plausibility and realism of human motion in AI-generated videos by measuring anatomical, kinematic, and kinetic correctness. It also assesses how well these automated metrics correlate with human preference judgments.

## Datasets

- **HumanScore Benchmark** — total ?; splits: test (-1)

## Metrics

- `Anatomy Correctness` — range: [0, 100]
  - Measures anatomical plausibility of reconstructed 3D poses, penalizing violations of joint limits and bone-length stability.
- `Kinematic Correctness` — range: [0, 100]
  - Evaluates temporal stability and motion smoothness, penalizing frame-wise jitter and motion drift in the recovered 3D trajectory.
- `Kinetic Correctness` **(primary)** — range: [0, 100]
  - Assesses overall biomechanical consistency including self-collision and physical plausibility, serving as the primary composite score.

## Input / output format

**Input**: Video clips (generated or real) containing a single person performing a specific motion.

**Output**: Numerical scores (0–100 scale) for each biomechanical dimension and a composite HumanScore.

## Scoring recipe

```python
def evaluate_video(video):
    poses, meshes = recover_3d_from_video(video)
    valid_frames = filter_by_confidence(poses, meshes)
    anatomy_score = penalize_joint_limits_and_bone_length(valid_frames)
    kinematic_score = penalize_jitter_and_motion_drift(valid_frames)
    kinetic_score = penalize_self_collision_and_physical_violations(valid_frames)
    humanscore = average(anatomy_score, kinematic_score, kinetic_score)
    return humanscore
```

## Common pitfalls

- Metrics rely on monocular 3D reconstruction, making them sensitive to occlusion, motion blur, and background clutter.
- Real-world videos do not achieve perfect scores due to inherent depth ambiguity and reconstruction noise causing mild frame-wise jitter.
- Atypical or extreme human poses in real videos may be penalized by conservative literature-derived biomechanical bounds.

## Evidence (verbatim from paper)

> The models are evaluated using our proposed biomechanics-informed metrics across three major dimensions: anatomy correctness, kinematic correctness, and kinetic correctness.

## Citation

```bibtex
@misc{fang2026humanscore,
  title={HumanScore: Benchmarking Human Motions in Generated Videos},
  author={Fang et al. (2026)},
  year={2026},
  note={arXiv:2604.20157}
}
```

- arXiv: 2604.20157