AI Artist with Reinforcement Learning and Graph Neural Network

Authors

  • Keliang Luo
  • Xinyi Wang
  • Qingfeng Zou

DOI:

https://doi.org/10.54097/hset.v39i.6694

Keywords:

AI Paint; Reinforcement Learning; Ai Artist.

Abstract

Recently, researchers have had much progress in the field of AI painting, inventing advanced machine learning models like Stable Diffusion. However, past methods mostly neglect the clients’ customized requests and need for humanized service. Facing this weakness, this paper constructs an AI to receive text input and to literally paint the image in a humanized way with a customized style. It analyzes the clients’ style requests and breaks down the need into two parts, the painting style, and the painting technique. After generating the initial image with Stable Diffusion models, this AI pre-process it with graph neural networks to satisfy the need for customized painting styles. Then, Reinforcement Learning is implemented to train the AI to paint the image like human painters with distinct strokes. In this step, the strokes can be modified to mimic various painting techniques like oil painting, pencil, etc. By two aspects of the style, this AI artist provides clients with a personalized painting with and a video of the full painting process.

Downloads

Download data is not yet available.

References

Khurana, D., Koli, A., Khatter, K., & Singh, S. Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 2022: 1-32.

Mazzone, M., & Elgammal, A. Art, creativity, and the potential of artificial intelligence. Arts, 2019, 8(1), 26.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. High-resolution image synthesis with Latent Diffusion Models. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, 1-11.

Cohen, P. (2016). Harold Cohen and AARON. Ai Magazine, 37(4), 63-66.

Little-Tetteh, K., & Shchyhelska, H. Artificial intelligence painting: is it art, really?. 2019, 73-75.

Cetinic, E., & She, J. Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications, 2022, 18(2), 1-22.

Liu, Z., Luo, P., Wang, X., & Tang, X. Deep learning face attributes in the wild. 2015 IEEE International Conference on Computer Vision, 2015.425.

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. Generative adversarial networks: An overview. IEEE signal processing magazine, 2018, 35(1), 53-65.

Aggarwal, A., Mittal, M., & Battineni, G. Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, 2021, 1(1), 100004.

Karras, T., Aittala, M., Aila, T., & Laine, S. Elucidating the Design Space of Diffusion-Based Generative Models. 2022 arXiv preprint arXiv:2206.00364.

Portilla, J., & Simoncelli, E. P. A parametric texture model based on joint statistics of complex wavelet coefficients. International journal of computer vision, 2000, 40(1), 49-70.

Simonyan, K., & Zisserman, A. Very deep convolutional networks for large-scale image recognition. 2014 arXiv preprint arXiv:1409.1556.

Gatys, L., Ecker, A., & Bethge, M. A neural algorithm of artistic style. Journal of Vision, 2016, 16 (12), 326.

Gatys, L., Ecker, A. S., & Bethge, M. Texture synthesis using convolutional neural networks. Advances in neural information processing systems, 2015, 28.

Huang, Z., Zhou, S., & Heng, W. Learning to paint with model-based deep reinforcement learning. 2019 IEEE/CVF International Conference on Computer Vision. 2019, 4378-4389.

Keiron O’Shea, Ryan Nash. An Introduction to Convolutional Neural Networks. arXiv: 1511.08458.

Binxu Wang, Understanding Stable Diffusion from "Scratch", Harvard Medical School, 2020 https:// scholar. harvard.edu/binxuw/classes/machine-learning-scratch/materials/stable-diffusion-scratch.

INNAT, Wiki-Art: Visual Art Encyclopedia, 2022, https://www.kaggle.com/datasets/ipythonx/wikiart-gangogh-creating-art-gan.

Downloads

Published

01-04-2023