Objective

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With the continuous development of digital media

people's demand for animation works continues to increase. Excellent two-dimensional animation works usually require a lot of time and effort. In the animation production process

the key frame line draft images are usually drawn by the original artist

then the intermediate frame line draft images are drawn by multiple ordinary animators

and finally all the line draft images are colored by the coloring staff. In order to improve the production efficiency of two-dimensional animation art

researchers have committed to improving the automation of the production process. At present

data-driven deep learning technology is developing rapidly

which provides a new solution for improving the production efficiency of animation works. Although many data-driven automated methods have been proposed

it is very difficult to obtain training datasets

and there is no public dataset that corresponds to color images and linear images. For this reason

the research work of automatically extracting line draft images from color animation images will provide data support for animation production-related research.

Method

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Early image edge extraction methods depend on the setting of parameter values

and fixed parameters cannot be applied to all images. However

the data-driven image edge extraction method is limited by the collection and size of the dataset. Therefore

researchers usually use data enhancement techniques or use images similar to line art

such as boundary images (edge information extracted from color images). This study proposes an automatic extraction model of linear art images based on the cycle-consistent adversarial networks to solve the problem of the difficulty of obtaining real line art images and the distortion of the existing line art image extraction methods. First of all

this study uses a cycle-consistent adversarial network structure to solve the dataset problem without real color images and corresponding line art images. It only uses a few collected real line art images and a large number of color images to learn the model parameters. Then

the mask-guided convolution unit and the mask-guided residual unit are proposed to better autonomously select the intermediate output features of the network. Specifically

the input images of different scales and their corresponding boundary images are input to mask-guided convolution unit to learn the mask parameters of the intermediate feature layer

where the boundary map determines the line area of the line art image and the input image provides prior information. In order to ensure that information is not lost in the process of information encoding

no operations such as pooling that can cause information loss are used in the network design process

but the image resolution is reduced by controlling the size of the convolution kernel and the convolution step length. Finally

this study proposes a boundary constraint loss function. Considering that this study does not have the supervision information corresponding to the input image

the loss function is designed to calculate the difference between the gradient information of the input image and the output image. At the same time

regular constraints are added to ensure that the generated result is consistent with the gradient of the input image. The proposed method mainly restricts the gradient of the input image and the generated image to be consistent.

Result

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Finally

on the public animation color image dataset Danbooru2018

the line art image extraction results of this method are compared with the results extracted by the Canny edge detection operator

cycle-consistent adversarial networks (CycleGAN)

holistically-nested edge detection (HED)

and SketchKeras methods. The Canny edge detection operator only extracts the position information of the image gradient. The resulting lines extracted by CycleGAN are blurred and accompanied by missing information

and the lines in some areas cannot be extracted correctly. The line art image extracted by HED has obvious outer contours but seriously lacks internal details. The line art image extracted by SketchKeras is closer to the edge information image and contains the rich gradient change information

which causes the lines to be unclear and noisy. The extracted results of the proposed model are not only clear and have less noise

but also are more in line with the effect drawn by human animators. In order to show the actual performance effect of the proposed method

30 users between the ages of 20-25 years are invited to score the cartoon line art images extracted by five different methods. A total of 30 sets of test samples are provided. Each user selects the best line art image in each group according to whether the extracted line art image lines are clear

whether there is noise

and whether it is close to the real cartoonist's line art image. The statistical results show that the linear art image extracted by the proposed method is superior to that of other methods in terms of image quality and authenticity. Moreover

the proposed method can not only extract the line art image corresponding to the color animation image

but also extract the line art from the real color image. In the experiment

the model was used to extract line art images from real-world color images

and results similar to animation line art images were obtained. At the same time

the proposed model is better at extracting black border lines

which may be because the borders of the color animation images given in the training set are black lines.

Conclusion

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This study proposes a model for extracting line art images from color animation images. It trains network parameters through asymmetric data and does not require a large amount of real cartoon line art images. The proposed mask-guided convolution unit and mask-guided residual unit constrain the output features of the intermediate network through the input image and the corresponding boundary image to obtain clearer line results. The proposed boundary consistency loss function introduces a Gaussian regular term to make the boundary of the region with severe gradient change more obvious

and the region with weak gradient change is smoother

reducing the noise in the generated line art image. Finally

the proposed method extracts corresponding line art images from the public animation color dataset Danbooru2018

provides data support for subsequent line art drawing and line art coloring research work

and can also extract results similar to the sketch drawn by an animator from the real color image.