Pewarnaan Otomatis Sketsa Gambar Menggunakan Metode Conditional GAN Untuk Mempercepat Proses Pewarnaan

Regan Reinaldo Kalendesang, Liliana Liliana, Djoni Haryadi Setiabudi


Anime is a Japanese animation that consists of many frames of images. Images that used to make an anime can be made using hand-drawn or using digital-drawn. It takes a lot of time to make an anime. In making anime for 1 second, it needs a total of 24 frames, this is why it takes a lot of time to make anime and also takes a lot of money. Each image also needs to be colored, this is also why making anime takes so much time. The method used in this research is GAN (Generative Adversarial Network) or should we call C-GAN (Conditional Generative Adversarial Network) to make coloring anime sketches easier. Dataset that is used in this research is a pair of sketch images and sketch images that have already been colored.


GAN; Generative Adversarial Network; Auto coloring; Conditional-GAN

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Bond, J.-M. 2021, January 27. A brief history of anime

genres, culture, and evolution. The Daily Dot. Retrieved

August 16, 2021, from

Bynagari, N. B. 2019. GANs Trained by a Two Time-Scale

Update Rule Converge to a Local Nash Equilibrium. Asian Journal of Applied Science and Engineering. 2305-915X(p); 2307-9584(e)

Ci, Y., Ma, X., Wang, Z., Li, H., & Luo, Z. 2018. Userguided deep anime line art colorization with conditional

adversarial networks. Proceedings of the 26th ACM

International Conference on Multimedia.

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K.,

Sengupta, B., & Bharath, A. A. 2018. Generative adversarial

networks: An overview. IEEE Signal Processing Magazine,

(1), 53–65.

Frans, K. 2017. Outline Colorization through Tandem

Adversarial Networks.

Goodfellow, I. J., Bengio, Y., Courville, A., Ozair, S.,

Warde-Farley, D., Xu, B., Mirza, M., & Pouget-Abadie, J.

Generative Adversarial Nets. Advances in Neural

Information Processing Systems.

Hensman, P., & Aizawa, K. 2017. cGAN-Based manga

Colorization using a single training image. 2017 14th IAPR

International Conference on Document Analysis and

Recognition (ICDAR).

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., &

Hochreiter, S. 2018. Advances in neural information

processing systems. GANs Trained by a Two Time-Scale

Update Rule Converge to a Local Nash Equilibrium.

Karras, T., Aila, T., Laine, S., & Lehtinen, J. 2018.

Progressive Growing of GANs for Improved Quality,

Stability, and Variation.

Kim, T. 2018, December 14. Anime sketch colorization pair.

Kaggle. Retrieved December 16, 2021, from

Liu, Y., Qin, Z., Wan, T., & Luo, Z. 2018. Auto-painter:

Cartoon image generation from sketch by using conditional

Wasserstein generative adversarial networks.

Neurocomputing, 311, 78–87.

Nazeri, K., Ng, E., & Ebrahimi, M. 2018. Image colorization

using generative adversarial networks. Articulated Motion

and Deformable Objects, 85–94.


Sun, Q., Chen, Y., Tao, W., Jiang, H., Zhang, M., Chen, K.,

& Erdt, M. 2021. A gan-based approach toward architectural

line Drawing colorization Prototyping. The Visual Computer.


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