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What do you think of the "trapped-ball segmentation" in this paper? #127
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Thanks for sharing information, personally I think using segmentation information is useful for colorization. Im not so confident about actual method, but one idea is making dataset of sketch to segment using photo segmentation data and line extraction network. |
Do you think "paintschainer" itself is actually, to some extent, already a good segment network? Because AI do not know what is the color of each image's hair, so the AI marks the hair yellow. |
hmm. |
https://arxiv.org/abs/1706.06759 Hmm, so many paper related to style/color ref colorization recently. |
Yes, the papers in this field is very competitive... |
By the way, how you set learning rate to your very giant 600,000 dataset? |
Actually I changed the learning rate manually... for fine tuning. |
cGAN-based Manga Colorization Using a Single Training Image
https://arxiv.org/abs/1706.06918
@powion
The author uses a "trapped-ball segmentation" to divide the painting into several parts, as a hint to generator. Though it may only handle with closed figures, to some extent, it is possiable to be helpful to make the final output sharp and clear.
However, the examples' inputs in this paper are not sketches but grayscale images with full texture and full shadow, such as:
Because it is very much easier to colorize such informative gray-scaled images than real sketches, maybe the final result will not be so well when the "trapped-ball segmentation" is attached to a sketch colorize AI.
But whether there are any methods to take the advantage of such "segmentation" when we colorize a real sketch?
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