Implementation for "Manga Filling Style Conversion with Screentone Variational Autoencoder" (SIGGRAPH ASIA 2020 issue)

Overview

Manga Filling with ScreenVAE

SIGGRAPH ASIA 2020 | Project Website | BibTex

This repository is for ScreenVAE introduced in the following paper "Manga Filling Style Conversion with Screentone Variational Autoencoder", SIGGRAPH AISA 2020 (TOG)

Introduction

Western color comics and Japanese-style screened manga are two popular comic styles. They mainly differ in the style of region-filling. However, the conversion between the two region-filling styles is very challenging, and manually done currently. In this paper, we identify that the major obstacle in the conversion between the two filling styles stems from the difference between the fundamental properties of screened region-filling and colored region-filling. To resolve this obstacle, we propose a screentone variational autoencoder, ScreenVAE, to map the screened manga to an intermediate do- main. This intermediate domain can summarize local texture characteristics and is interpolative. With this domain, we effectively unify the properties of screening and color-filling, and ease the learning for bidirectional translation between screened manga and color comics. To carry out the bidirectional translation, we further propose a network to learn the translation between the intermediate domain and color comics. Our model can generate quality screened manga given a color comic, and generate color comic that retains the original screening intention by the bitonal manga artist. Several results are shown to demonstrate the effectiveness and convenience of the proposed method. We also demonstrate how the intermediate domain can assist other applications such as manga inpainting and photo-to-comic conversion.

Citation

If you find the code helpful in your resarch or work, please cite the following papers.

@article{xie-2020-manga,
        author   = {Minshan Xie and Chengze Li and Xueting Liu and Tien-Tsin Wong},
        title    = {Manga Filling Style Conversion with Screentone Variational Autoencoder},
        journal  = {ACM Transactions on Graphics (SIGGRAPH Asia 2020 issue)},
        month    = {December},
        year     = {2020},
        volume   = {39},
        number   = {6},
        pages    = {226:1--226:15}
    }
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Comments
  • Some problems in code

    Some problems in code

    Your code requires the image to be grey and have a size that is divisible by 16, but there is no sanitization after reading image. Therefore, the code is very likely to throw error. We can tackle this by reading the image with PIL and pad it:

    import numpy as np
    from PIL import Image
    
    def PILopen(path, astype='L'):
        return np.asarray(Image.open(str(path)).convert(astype))
    def modpad(img, mods):
        imgy, imgx = img.shape
        mody, modx = mods
        pady = mody-imgy%mody; padx = modx-imgx%modx
        padded = np.pad(img, ((0, pady), (0, padx)), 'linear_ramp')
        return padded
    
    image = PILopen('path/to/image')
    impad = modpad(image, (16,16))
    # codes here
    ...
    sizey, sizex = image.shape
    result = impad[0:sizey, 0:sizex]
    

    Further reading of code shows that padding have been done internally:

        def npad(self, im, pad=128):
            h,w = im.shape[-2:]
            hp = h //pad*pad+pad
            wp = w //pad*pad+pad
            return F.pad(im, (0, wp-w, 0, hp-h), mode='constant',value=1)
    

    but it only work in dec() and there's no padding in enc()

    opened by TsXor 9
  • Wrong testing results

    Wrong testing results

    Please check if all the checkpoints are successfully loaded. I just updated the code as the previous one won't print out error messages when the checkpoints are not found.

    Sorry for the inconvenience caused.

    opened by msxie92 0
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