README.md

---
pretty_name: CaptureGuide-Bench
language:
- en
- zh
license: other
size_categories:
- n<1K
tags:
- image
- benchmark
- photography
- multimodal
- vision-language
- composition-recommendation
- pose-recommendation
- capture-time-guidance
- arxiv:2606.25763
viewer: false
---

# CaptureGuide-Bench

[![arXiv](https://img.shields.io/badge/arXiv-2606.25763-b31b1b.svg)](https://arxiv.org/abs/2606.25763)
[![HF Paper](https://img.shields.io/badge/Paper-HuggingFace-yellow.svg)](https://huggingface.co/papers/2606.25763)
[![Project Page](https://img.shields.io/badge/Project-ShutterMuse-blue.svg)](https://lijayuTnT.github.io/ShutterMuse/)
[![GitHub](https://img.shields.io/badge/GitHub-lijayuTnT%2FShutterMuse-black.svg)](https://github.com/lijayuTnT/ShutterMuse)
[![Model](https://img.shields.io/badge/Model-ShutterMuse-orange.svg)](https://huggingface.co/ShutterMuse/ShutterMuse)

CaptureGuide-Bench is the evaluation benchmark for **ShutterMuse: Capture-Time Photography Guidance with MLLMs**. It evaluates capture-time photography guidance from two complementary perspectives:

- **Photographer-side / Composition Recommendation:** given the current framing, decide whether it should be kept, refined, or rejected, and recommend composition boxes when refinement is needed.
- **Subject-side / Pose Recommendation:** given a scene image, recommend a scene-conditioned portrait pose. ShutterMuse predicts COCO-17 human keypoints with visibility states and can render the recommended pose for evaluation.

## Links

| Resource | Link |
| --- | --- |
| Paper | [arXiv:2606.25763](https://arxiv.org/abs/2606.25763) |
| Paper Page | [Hugging Face Papers](https://huggingface.co/papers/2606.25763) |
| Project Page | [ShutterMuse Project](https://lijayuTnT.github.io/ShutterMuse/) |
| GitHub Repository | [lijayuTnT/ShutterMuse](https://github.com/lijayuTnT/ShutterMuse) |
| ShutterMuse Model | [ShutterMuse/ShutterMuse](https://huggingface.co/ShutterMuse/ShutterMuse) |

## Dataset Structure

```text
CaptureGuide-Bench/
├── photographer-side/
│   └── composition_benchmark/
│       ├── meta_new.json
│       └── original_composition/
└── subject-side/
    ├── paper-benchmark/
    └── paper-benchmark-gt/
```

### Photographer-side: Composition Recommendation

- **Task:** evaluate composition decision and refinement quality.
- **Images:** `photographer-side/composition_benchmark/original_composition/` contains 421 benchmark images.
- **Annotations:** `photographer-side/composition_benchmark/meta_new.json` stores image-level composition metadata, textual rationales, scores, and optional composition boxes.
- **Main fields:**
  - `origin`: rationale fields for the original framing, including good/bad reasons and composition type labels.
  - `composition_boxes`: candidate refinement boxes. Each `rect` is normalized as `[x1, y1, x2, y2]`.
  - `score`: composition quality score used by the benchmark scripts.

### Subject-side: Pose Recommendation

- **Task:** evaluate scene-conditioned portrait pose recommendation.
- **Input images:** `subject-side/paper-benchmark/` contains 552 scene images.
- **Reference images:** `subject-side/paper-benchmark-gt/` contains the paired reference/ground-truth images with matching file names.
- **Evaluation:** the ShutterMuse evaluation code compares generated pose visualizations against the paired references using VLM-based and pose-quality metrics.

## Download

### Download the full benchmark

```bash
hf download ShutterMuse/CaptureGuide-Bench \
  --repo-type dataset \
  --local-dir CaptureGuide-Bench
```

### Download only the photographer-side split

```bash
hf download ShutterMuse/CaptureGuide-Bench \
  --repo-type dataset \
  --include "photographer-side/*" \
  --local-dir CaptureGuide-Bench
```

### Download only the subject-side split

```bash
hf download ShutterMuse/CaptureGuide-Bench \
  --repo-type dataset \
  --include "subject-side/*" \
  --local-dir CaptureGuide-Bench
```

### Download with Python

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ShutterMuse/CaptureGuide-Bench",
    repo_type="dataset",
    local_dir="CaptureGuide-Bench",
)
```

If you use the official ShutterMuse evaluation scripts, place or symlink the downloaded benchmark as `Benchmark/` under the ShutterMuse repository root:

```bash
git clone https://github.com/lijayuTnT/ShutterMuse.git
cd ShutterMuse
hf download ShutterMuse/CaptureGuide-Bench --repo-type dataset --local-dir Benchmark
```

The expected paths are:

```text
ShutterMuse/Benchmark/photographer-side/composition_benchmark/meta_new.json
ShutterMuse/Benchmark/photographer-side/composition_benchmark/original_composition/
ShutterMuse/Benchmark/subject-side/paper-benchmark/
ShutterMuse/Benchmark/subject-side/paper-benchmark-gt/
```

## Usage with ShutterMuse Evaluation

Install the project dependencies following the [GitHub repository](https://github.com/lijayuTnT/ShutterMuse), then run one benchmark target at a time:

```bash
bash evaluation/scripts/run_unified_evaluation.sh photographer-model
bash evaluation/scripts/run_unified_evaluation.sh photographer-baseline
bash evaluation/scripts/run_unified_evaluation.sh subject
bash evaluation/scripts/run_unified_evaluation.sh subject-baseline
```

Common environment variables include `OUTPUT_ROOT`, `PYTHON_BIN`, model checkpoint paths, LoRA checkpoint paths, and API keys such as `GEMINI_API_KEY`, `QWEN_API_KEY`, and `GPT_API_KEY` for VLM-based scoring or API baselines.

## Citation

If you use CaptureGuide-Bench, please cite:

```bibtex
@misc{li2026shuttermuse,
  title        = {ShutterMuse: Capture-Time Photography Guidance with MLLMs},
  author       = {Li, Jiayu and Fang, Yixiao and Hu, Tianyu and Cheng, Wei and Huang, Ping and Fan, Zheheng and Yu, Gang and Ma, Xingjun},
  year         = {2026},
  eprint       = {2606.25763},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2606.25763}
}
```

## License

The benchmark is released for research use. Please check the project repository and dataset repository for the latest license and usage terms.