An Interactive Collaborative Creation System for Shadow Puppets Based on Smooth Generative Adversarial Networks
Source abstract
Abstract
Chinese shadow puppetry has been recognized as a world intangible cultural heritage. However, it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow puppets. To ensure the inheritance and development of this cultural heritage, it is imperative to enable traditional art to flourish in the digital era. This paper presents an Interactive Collaborative Creation System for shadow puppets, designed to facilitate the creation of high-quality shadow puppet images with greater ease. The system comprises four key functions: Image contour extraction, intelligent reference recommendation, generation network, and color adjustment, all aimed at assisting users in various aspects of the creative process, including drawing, inspiration, and content generation. Additionally, we propose an enhanced algorithm called Smooth Generative Adversarial Networks (SmoothGAN), which exhibits more stable gradient training and a greater capacity for generating high-resolution shadow puppet images. Furthermore, we have built a new dataset comprising high-quality shadow puppet images to train the shadow puppet generation model. Both qualitative and quantitative experimental results demonstrate that SmoothGAN significantly improves the quality of image generation, while our system efficiently assists users in creating high-quality shadow puppet images, with a SUS scale score of 84.4. This study provides a valuable theoretical and practical reference for the digital creation of shadow puppet art.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Generative Adversarial Networks and Image Synthesis · Human Motion and Animation · 3D Shape Modeling and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (38)
Show source-provided references
1. ShadowStory: Creative and collaborative digital storytelling inspired by cultural heritage
2. Research on digital protection and inheritance measures of shaanxi shadow art based on new media times
3. Research on a method of creating digital shadow puppets based on parameterized templates10.1007/s11042-021-10726-1 ↗
4. Semantic framework for interactive animation generation and its application in virtual shadow play performance10.1007/s10055-018-0333-8 ↗
5. The interactive narration of Chinese shadow play
6. Restoration of traditional Chinese shadow play-Piying art from tangible interaction: Transform Chinese shadow play into an interactive system10.1002/cav.1530 ↗
7. Virtual scene generation promotes shadow puppet art conservation10.1002/cav.2148 ↗
8. A method for traditional shadow play figure’s head regeneration based on generative adversarial network
9. R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” 2022. Accessed: Dec. 12, 2023. [Online]. Available: http://arxiv.org/abs/2112.10752.
10. An interactive and generative approach for Chinese Shanshui painting document
11. A. Xue, “End-to-End Chinese landscape painting creation using generative adversarial networks,” 2020. Accessed: Nov. 20, 2021. [Online]. Available: http://arxiv.org/abs/2011.05552
12. TwinGAN: Twin generative adversarial network for chinese landscape painting style transfer10.1109/ACCESS.2023.3274666 ↗
13. Z. Wang, J. Zhang, Z. Ji, J. Bai, and S. Shan, “CCLAP: Controllable Chinese landscape painting generation via latent diffusion model,” 2023. Accessed: Dec. 14, 2023. [Online]. Available: http://arxiv.org/abs/2304.04156.10.1109/ICME55011.2023.00362 ↗
14. Generating handwritten Chinese characters using CycleGAN
15. SCFont: Structure-guided Chinese font generation via deep stacked networks
16. Chinese handwriting imitation with hierarchical generative adversarial network
17. FontRNN: Generating large-scale chinese fonts via recurrent neural network10.1111/cgf.13861 ↗
18. Separating style and content for generalized style transfer
19. Visual knowledge guided intelligent generation of Chinese seal carving10.1631/FITEE.2100094 ↗
20. L. A. Gatys, A. S. Ecker, and M. Bethge, “A neural algorithm of artistic style,” 2015. Accessed: Feb. 02, 2023. [Online]. Available: http://arxiv.org/abs/1508.06576.
21. J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” 2016. Accessed: Feb. 02, 2023. [Online]. Available: http://arxiv.org/abs/1603.08155.
22. F. Luan, S. Paris, E. Shechtman, and K. Bala, “Deep photo style transfer,” 2017. Accessed: Feb. 02, 2023. [Online]. Available: http://arxiv.org/abs/1703.07511.10.1109/CVPR.2017.740 ↗
23. L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” 2023. Accessed: Dec. 12, 2023. [Online]. Available: http://arxiv.org/abs/2302.05543.10.1109/ICCV51070.2023.00355 ↗
24. Generative adversarial networks10.1145/3422622 ↗
25. Image-to-Image translation with conditional adversarial networks
26. J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” 2020. Accessed: Oct. 26, 2022. [Online]. Available: http://arxiv.org/abs/1703.10593.
27. J. Y. Zhu et al., “Toward multimodal image-to-image translation,” 2018. Accessed: Nov. 15, 2022. [Online]. Available: http://arxiv.org/abs/1711.11586.
28. Unsupervised Image-to-Image translation with stacked cycle-consistent adversarial networks
29. Cubic convolution interpolation for digital image processing10.1109/TASSP.1981.1163711 ↗
30. A computational approach to edge detection
31. T. C. Wang, M. Y. Liu, J. Y. Zhu, A. Tao, J. Kautz and B. Catanzaro, “High-resolution image synthesis and semantic manipulation with conditional GANs,” 2018. Accessed: Dec. 14, 2022. [Online]. Available: http://arxiv.org/abs/1711.11585.
32. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” 2014. Accessed: Feb. 12, 2023. [Online]. Available: http://arxiv.org/abs/1311.2524.
33. Convolutional sketch inversion10.1007/978-3-319-46604-0_56 ↗
34. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2015. Accessed: Feb. 13, 2023. [Online]. Available: http://arxiv.org/abs/1512.03385.
35. M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local nash equilibrium,” 2018. Accessed: Jan. 30, 2024. [Online]. Available: http://arxiv.org/abs/1706.08500.
36. Image quality assessment: From error visibility to structural similarity10.1109/TIP.2003.819861 ↗
37. You help but only with enough details: Understanding user experience of co-creation with artificial intelligence
38. Painting with Bob: Assisted creativity for novices