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---

# Complex Virtual Dressing Dataset (CVDD)
This dataset contains 516 pairs of virtual try-on test data in complex scenarios, as proposed in the paper ["FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on"](https://arxiv.org/abs/2411.10499).


The images in this dataset have varying resolutions. To ensure consistent testing under the same resolution, you can use the following code:
```
from PIL import Image
import math

def pad_and_resize(im, new_width=768, new_height=1024, pad_color=(255, 255, 255), mode=Image.LANCZOS):
    old_width, old_height = im.size
    
    ratio_w = new_width / old_width
    ratio_h = new_height / old_height
    if ratio_w < ratio_h:
        new_size = (new_width, round(old_height * ratio_w))
    else:
        new_size = (round(old_width * ratio_h), new_height)
    
    im_resized = im.resize(new_size, mode)

    pad_w = math.ceil((new_width - im_resized.width) / 2)
    pad_h = math.ceil((new_height - im_resized.height) / 2)

    new_im = Image.new('RGB', (new_width, new_height), pad_color)
    
    new_im.paste(im_resized, (pad_w, pad_h))

    return new_im, pad_w, pad_h


def unpad_and_resize(padded_im, pad_w, pad_h, original_width, original_height):
    width, height = padded_im.size

    left = pad_w
    top = pad_h
    right = width - pad_w
    bottom = height - pad_h
    
    cropped_im = padded_im.crop((left, top, right, bottom))

    resized_im = cropped_im.resize((original_width, original_height), Image.LANCZOS)

    return resized_im


# resize image to same resolution(e.g., 768x1024)
image_size = image.size
image, pad_w, pad_h = pad_and_resize(image, 768, 1024)

# convert back to original size
image = unpad_and_resize(image, pad_w, pad_h, image_size[0], image_size[1])
```