The Interactive Narration of Chinese Shadow Play

Yan, Zifei; Jia, Ziyuan; Chen, Yuehua; Ding, Haolun

doi:10.1109/icvrv.2016.63

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Previous research predominantly concentrates on shadow puppetry performances [4][5][6], with less focus on the intelligent creation of shadow puppet images. The intelligent creation of shadow puppetry poses numerous challenges.…”

Section: Introductionmentioning

confidence: 99%

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

Yang,

Lou,

Chen

et al. 2024

CMC

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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 highresolution 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.

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Section: Introductionmentioning

confidence: 99%