Browse > Article
http://dx.doi.org/10.7583/JKGS.2018.18.2.89

2D Game Image Color Synthesis System Using Convolutional Neural Network  

Hong, Seung Jin (School of Games, Hongik University)
Kang, Shin Jin (School of Games, Hongik University)
Cho, Sung Hyun (School of Games, Hongik University)
Publication Information
Journal of Korea Game Society / v.18, no.2, 2018 , pp. 89-98 More about this Journal
Abstract
The recent Neural Network technique has shown good performance in content generation such as image generation in addition to the conventional classification problem and clustering problem solving. In this study, we propose an image generation method using artificial neural network as a next generation content creation technique. The proposed artificial neural network model receives two images and combines them into a new image by taking color from one image and shape from the other image. This model is made up of Convolutional Neural Network, which has two encoders for extracting color and shape from images, and a decoder for taking all the values of each encoder and generating a combination image. The result of this work can be applied to various 2D image generation and modification works in game development process at low cost.
Keywords
Procedural Contents Generation; Neural Network; Autoencoder;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems, pp1097-1105, 2012.
2 Mikolov, Tomáš, et al. "Recurrent neural network based language model." Eleventh Annual Conference of the International Speech Communication Association, 2010.
3 Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8, pp.1735-1780, 1997.   DOI
4 Dauphin, Y., de Vries, H., & Bengio, Y. Equilibrated adaptive learning rates for non-convex optimization. In Advances in neural information processing systems, pp. 1504-1512, 2015.
5 Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576, 2015
6 Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems, pp.2672-2680, 2014.
7 Dosovitskiy, A., Springenberg, J. T., & Brox, T. (2015, June). Learning to generate chairs with convolutional neural networks. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on pp.1538-1546, 2015.
8 Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." science 313.5786, pp.504-507, 2006.   DOI
9 Jain, Rishabh, et al. "Autoencoders for level generation, repair, and recognition." Proceedings of the ICCC Workshop on Computational Creativity and Games, 2016.
10 Xue, Tianfan, et al. "Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks." Advances in Neural Information Processing Systems, pp.91-99, 2016.
11 Reed, Scott E., et al. "Deep visual analogy-making." Advances in neural information processing systems, pp.1252-1260, 2015.