Garments2look Eval

---
name: garments2look-eval
description: Probes the ability of virtual try-on and image editing models to synthesize high-fidelity, multi-reference outfit images. It evaluates whether models can preserve fine-grained garment details, maintain correct layering orders, and adhere to specific styling techniques while keeping the target person's pose consistent. Use when the user wants to benchmark on Garments2Look, DressCode-MR, or asks about evaluating this task. Reports FID↓.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2603.14153
  bibtex_key: [REDACTED]
  confidence: high
---

# garments2look-eval

> Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories — Hu et al. (2026) (arXiv:2603.14153, 2026)

## What this evaluates

Probes the ability of virtual try-on and image editing models to synthesize high-fidelity, multi-reference outfit images. It evaluates whether models can preserve fine-grained garment details, maintain correct layering orders, and adhere to specific styling techniques while keeping the target person's pose consistent.

## Datasets

- **Garments2Look** — total 80000; splits: test (-1); repo https://github.com/ArtmeScienceLab/Garments2Look
- **DressCode-MR** — total ?; splits: test (-1)

## Metrics

- `FID↓` **(primary)** — range: other
  - Fréchet Inception Distance measures the distance between feature distributions of real and generated images. Lower is better.
- `KID↓` — range: other
  - Kernel Inception Distance uses polynomial kernel statistics to compare image feature distributions. Lower is better.
- `SSIM↑` — range: [0, 1]
  - Structural Similarity Index measures perceived change in structural information between images. Higher is better.
- `LPIPS↓` — range: [0, 1]
  - Learned Perceptual Image Patch Similarity computes perceptual distance using deep network activations. Lower is better.
- `Garment accuracy` — range: percent
  - Binary accuracy score judged by a Vision-Language Model (VLM) on whether the generated garment matches the reference.
- `Layering accuracy` — range: percent
  - Binary accuracy score judged by a VLM on whether the generated layering order matches the ground truth.
- `Styling accuracy` — range: percent
  - Binary accuracy score judged by a VLM on whether the generated styling technique matches the reference.

## Input / output format

**Input**: Target person image (OOTD), one or multiple reference garment images, and optionally text prompts or skeleton/pose maps.

**Output**: A single synthetic try-on image combining the target person's pose/body with the reference garments.

## Scoring recipe

```python
# Pseudo-code for evaluation protocol
generated_images = model.predict(person_img, ref_garments, prompt)
fid_score = compute_fid(real_images, generated_images)
kid_score = compute_kid(real_images, generated_images)
ssim_score = compute_ssim(real_images, generated_images)
lpips_score = compute_lpips(real_images, generated_images)
garment_acc = vlm_judge(generated_images, ref_garments, "garment_match")
layering_acc = vlm_judge(generated_images, gt_layering, "layering_match")
styling_acc = vlm_judge(generated_images, gt_styling, "styling_match")
```

## Common pitfalls

- Pose inconsistency heavily penalizes FID/LPIPS even if garment fidelity is high, as standard VTON metrics are sensitive to pose preservation.
- VLM-based accuracy metrics for layering and styling lack standardized prompts, risking evaluator bias or inconsistent grading.
- Multi-reference input strategy (N Ref vs 2 Ref) significantly impacts results; models often fuse items or drop layers when cardinality exceeds 4.

## Evidence (verbatim from paper)

> Table 3: Quantitative comparison on Garments2Look test set. We report results on classical VTON metrics and accuracy metrics judged by VLM. Models | FID↓ | KID↓ | SSIM↑ | LPIPS↓ | Garment↑ | Layering↑ | Styling↑

## Citation

```bibtex
@misc{hu2026garments2look,
  title={Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories},
  author={Hu et al. (2026)},
  year={2026},
  note={arXiv:2603.14153}
}
```

- arXiv: 2603.14153