# Geco

> Evaluates geometric consistency in text-to-video generation by measuring structural and motion coherence across camera trajectories, detecting deformation and occlusion artifacts in static scenes.

- Skill: `qhjqhj00/geco` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/geco`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/geco/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, Model Training & Fine-tuning
- Tags: Evaluation Metric, Fused, Geometric Consistency, Motion, Structure, Video Generation
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/qhjqhj00/geco

---


# geco

> GeCo: A Differentiable Geometric Consistency Metric for Video Generation — Gu et al. (2025) (arXiv:2512.22274, 2025)

## What this evaluates

Evaluates geometric consistency in text-to-video generation by measuring structural and motion coherence across camera trajectories, detecting deformation and occlusion artifacts in static scenes.

## Datasets

- **GeCo-Eval** — total 80; splits: test (80)

## Metrics

- `Structure` — range: [0, 1]
  - Measures structural inconsistency (e.g., deformation, occlusion) averaged over pixels with valid depth and covisibility within evaluation windows. Lower is better.
- `Motion` — range: [0, 1]
  - Measures motion inconsistency (e.g., non-rigid drift) averaged over valid pixels. Lower is better.
- `Fused` **(primary)** — range: [0, 1]
  - Combines structure and motion inconsistency scores into a single geometric consistency metric. Lower is better.

## Input / output format

**Input**: Text prompt and generated video frames (resampled to ≤8 FPS), with depth maps and covisibility masks computed for evaluation.

**Output**: Scalar geometric consistency score per clip (Structure, Motion, or Fused), computed as a frame-weighted average over overlapping 3-second windows.

## Scoring recipe

```python
def compute_geco_score(video, fps=8):
    windows = extract_overlapping_3s_windows(video, fps)
    clip_scores = []
    for w in windows:
        mask = get_valid_depth_and_covisibility(w)
        struct_err = compute_structure_inconsistency(w, mask)
        motion_err = compute_motion_inconsistency(w, mask)
        fused_err = fuse(struct_err, motion_err)
        clip_scores.append(fused_err)
    return frame_weighted_average(clip_scores)
```

## Common pitfalls

- High consistency scores can be artificially inflated by videos with very low motion; the protocol requires reporting normalized motion statistics (Total Motion, Mean Motion) to disambiguate.
- Evaluation must be restricted to overlapping ~3-second windows resampled to ≤8 FPS to ensure fair comparison across models with different native generation settings.
- All GeCo metrics are lower-is-better (inconsistency/error), which is the opposite of standard accuracy metrics.

## Evidence (verbatim from paper)

> Within each window, we compute motion, structure, and fused scores averaged over pixels with valid depth and covisibility. Final clip-level scores are derived from the frame-weighted average of these windows, and we report the mean across all 320 clips (80 prompts × 4 seeds) per model.

## Citation

```bibtex
@misc{gu2025geco,
  title={GeCo: A Differentiable Geometric Consistency Metric for Video Generation},
  author={Gu et al. (2025)},
  year={2025},
  note={arXiv:2512.22274}
}
```

- arXiv: 2512.22274