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

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

Authors

Bailin Yang

Zheng Chen

Haoqiang Sun

Jianlu Cai



Contributors

Bin Sheng
Editor

Lei Bi
Editor

Jinman Kim
Editor

Nadia Magnenat-Thalmann
Editor

Daniel Thalmann
Editor

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). DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method. In B. Sheng, L. Bi, J. Kim, N. Magnenat-Thalmann, & D. Thalmann (Eds.), Advances in Computer Graphics 40th Computer Graphics International Conference, CGI 2023, Shanghai, China, August 28–September 1, 2023, Proceedings, Part II (479-490). https://doi.org/10.1007/978-3-031-50072-5_38

Conference Name CGI 2023: Advances in Computer Graphics
Conference Location Shanghai, China
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
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