To run this app locally, clone repo and do the following:
conda create --name vton python==3.8
pip install -r requirements.txt
python app.py| Method | SSIM (↑) | FID (↓) |
|---|---|---|
| Wardrobe-GAN | 0.740 | 47.34 |
| VITON | 0.783 | 55.71 |
| CP-VTON | 0.745 | 24.43 |
| ACGPN | 0.845 | 16.64 |
| Ours | 0.886 | 13.46 |
SSIM measures the structural similarity between the generated try-on image and the ground truth image. In virtual try-on systems:
- Range: 0 to 1 (higher is better)
- Purpose: Evaluates how well the generated image preserves structural details, textures, and overall visual quality
- Importance: Critical for VTON as it ensures the clothing item maintains its realistic appearance and proper fit on the person
- What it captures: Luminance, contrast, and structural information between images
FID measures the quality and diversity of generated images by comparing feature distributions between real and generated images:
- Range: 0 to ∞ (lower is better)
- Purpose: Evaluates the overall realism and quality of the generated try-on results
- Importance: Ensures that the virtual try-on images are indistinguishable from real photos and maintains natural appearance
- What it captures: Feature-level similarity using deep neural network representations, capturing both quality and diversity
- SSIM ensures the clothing details, patterns, and textures are preserved accurately during the virtual fitting process
- FID guarantees that the final try-on image looks realistic and natural, avoiding artifacts or unnatural distortions
- Together, they provide a comprehensive evaluation of both structural fidelity and perceptual quality in virtual try-on systems
Our method achieves state-of-the-art performance with the highest SSIM (0.886) and lowest FID (13.46), demonstrating superior quality in both structural preservation and realistic image generation.