VTONQA: A Multi-Dimensional Quality Assessment Dataset for Virtual Try-On

📌 Overview

VTONQA is a benchmark dataset for evaluating the quality of virtual try-on (VTON) results.
It provides multi-dimensional human annotations to assess realism, alignment, and perceptual quality of synthesized try-on images.

Compared with existing VTON datasets, VTONQA focuses on quality assessment rather than generation, enabling standardized evaluation across different methods.

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🎯 Key Features

• Multi-dimensional quality scores (e.g., realism, alignment, overall)
• Human-annotated evaluation labels
• Designed for benchmarking VTON models
• Supports both regression and ranking tasks

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🧾 Data Description

Input Data (dataset/)

The input data follows a standard virtual try-on (VTON) pipeline and consists of multiple modalities describing human appearance, clothing, and structural information.

Directory Structure

dataset/
├── image/ # original person images
├── cloth/ # clothing images
├── cloth_mask/ # binary masks for clothing
├── agnostic/ # person images with clothing removed
├── agnostic_mask/ # masks for removed clothing regions
├── image-parse/ # human parsing (semantic segmentation)
├── densepose_gray/ # DensePose representation
├── openpose_img/ # visualized human pose
├── openpose_json/ # pose keypoints (JSON format)
├── test_pairs_unpaired.txt

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Components Description

• image/:
  Original images of persons wearing their initial clothing.
  These provide identity, pose, and background information.

• cloth/:
  Standalone images of garments to be transferred onto the person.

• cloth_mask/:
  Binary masks for clothing images, where foreground indicates the clothing region.
  Used to improve garment alignment and shape modeling.

• agnostic/:
  Person images with original clothing removed while preserving identity-related regions such as face and hair.
  Serves as a clean input for try-on generation.

• agnostic_mask/:
  Masks indicating the regions where clothing has been removed.
  Helps guide the generation of new garments.

• image-parse/:
  Human parsing maps that segment the body into semantic regions (e.g., upper body, arms, background).
  Provides structural guidance for clothing placement.

• densepose_gray/:
  DensePose representations encoding fine-grained human body geometry.
  Enables better alignment between clothing and body surface.

• openpose_img/:
  Visualized human skeletons representing pose structure.

• openpose_json/:
  2D keypoint coordinates of human pose in JSON format.
  Provides precise numerical pose information.

• test_pairs_unpaired.txt:
  Defines the pairing between person images and clothing items.

  Example:

  167_whole.jpg d1.jpg
  283_whole.jpg d1.jpg
  85_whole.jpg d1.jpg
  195_whole.jpg d1.jpg
  

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Generated Results (result/)

The result/ directory contains outputs generated by multiple state-of-the-art virtual try-on (VTON) methods, as well as predefined evaluation splits for different quality dimensions.

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📂 Directory Structure

result/
├── CAT-DM/
├── CatV2TON/
├── DS-VITON/
├── FS-VTON/
├── keling/
├── ladivton/
├── LINKFOX/
├── OOTDiffusion/
├── StableVITON/
├── TPD/
├── VITON-HD/
│
├── body_compatibility_test.txt
├── body_compatibility_train.txt
├── clothing_fit_test.txt
├── clothing_fit_train.txt
├── overall_quality_test.txt
├── overall_quality_train.txt

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🧾 Method Outputs

Each subfolder corresponds to a specific VTON method:

• CAT-DM/, CatV2TON/, DS-VITON/, FS-VTON/, VITON-HD/, etc.

Each folder contains generated try-on images for the same set of input pairs.

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📊 Evaluation Splits

The dataset provides predefined splits for different evaluation dimensions:

• body_compatibility_*.txt
  → Evaluates how well the clothing matches the human body structure

• clothing_fit_*.txt
  → Evaluates how naturally the clothing fits the person

• overall_quality_*.txt
  → Evaluates overall perceptual quality

Each split includes:

• *_train.txt: training subset (for learning-based evaluators)
• *_test.txt: testing subset (for evaluation)

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📄 Split File Format

Each .txt file contains a list of sample identifiers.

Example: 523_lower_tr10_DS-VITON.png, 54.447846865424935 85_lower_tr6_OOTDiffusion.png, 37.64992522777412 510_upper_shirt10_ladivton.png, 56.459661598182684