DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method

Yang, Bailin; Chen, Zheng; Li, Frederick W. B.; Sun, Haoqiang; Cai, Jianlu

Abstract

We present a novel approach for modeling artists' drawing processes using an architecture that combines an unconditional generative adversarial network (GAN) with a multi-view generator and multi-discriminator. Our method excels in synthesizing various types of picture drawing, including line drawing, shading, and color drawing, achieving high quality and robustness. Notably, our approach surpasses the existing state-of-the-art unconditional GANs. The key novelty of our approach lies in its architecture design, which closely resembles the typical sequence of an artist's drawing process, leading to significantly enhanced image quality. Through experimental results on few-shot datasets, we demonstrate the potential of leveraging a multi-view generative model to enhance feature knowledge and modulate image generation processes. Our proposed method holds great promise for advancing AI in the visual arts field and opens up new avenues for research and creative practices.

Citation

Yang, B., Chen, Z., Li, F. W. B., Sun, H., & Cai, J. (2023, August). DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method. Presented at CGI 2023: Advances in Computer Graphics, Shanghai, China

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: CGI 2023: Advances in Computer Graphics
• Start Date: Aug 28, 2023
• End Date: Sep 1, 2023
• Acceptance Date: Jun 9, 2023
• Online Publication Date: Dec 29, 2023
• Publication Date: Dec 29, 2023
• Deposit Date: Sep 12, 2023
• Publicly Available Date: Dec 30, 2024
• Print ISSN: 0302-9743
• Publisher: Springer Nature
• Volume: 14496
• Pages: 479-490
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743
• Book Title: Advances in Computer Graphics 40th Computer Graphics International Conference, CGI 2023, Shanghai, China, August 28–September 1, 2023, Proceedings, Part II
• ISBN: 9783031500718
• DOI: https://doi.org/10.1007/978-3-031-50072-5_38
• Public URL: https://durham-repository.worktribe.com/output/1735773