Open Access

ARTICLE

An Interactive Collaborative Creation System for Shadow Puppets Based on Smooth Generative Adversarial Networks

Cheng Yang^1,2, Miaojia Lou^2,*, Xiaoyu Chen^1,2, Zixuan Ren¹

1 Department of Industrial Design, Hangzhou City University, Hangzhou, 310000, China
2 College of Computer Science and Technology, Zhejiang University, Hangzhou, 310000, China

* Corresponding Author: Miaojia Lou. Email:

(This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)

Computers, Materials & Continua 2024, 79(3), 4107-4126. https://doi.org/10.32604/cmc.2024.049183

Received 29 December 2023; Accepted 28 March 2024; Issue published 20 June 2024

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.

---

Keywords

---