A Generative AI Framework for Structural Analysis and DCGAN-Based Synthesis of Traditional Chinese Papercutting Patterns
- 1Solux College of Architecture & Design, University of South China, Hengyang 421001, China
- 2School of Art & Design, Guangzhou College of Commerce, Guangzhou 511363, China
Abstract
Traditional Chinese papercutting is an important form of intangible cultural heritage characterized by intricate structures, repeated motifs, and prominent symmetrical organization. Existing digital studies of traditional art commonly emphasize classification, restoration, style transfer, or visual reconstruction, while computational frameworks that combine interpretable structural characterization with generative exploration remain comparatively limited. This study therefore presents an exploratory framework integrating image standardization, quantitative structural-feature analysis, Principal Component Analysis (PCA), and Deep Convolutional Generative Adversarial Network (DCGAN)-based synthesis. The image corpus was standardized by grayscale conversion, Gaussian filtering, Otsu thresholding, morphological processing, resizing, and normalization. Foreground occupancy, edge density, connected-component complexity, and horizontal and vertical symmetry were extracted as interpretable structural descriptors. PCA was applied to the resulting feature matrix to examine variation among samples. The DCGAN used a 100-dimensional latent vector and generated 64 × 64 single-channel outputs; training progression was documented through generator/discriminator loss curves and fixed-latent-vector samples up to epoch 2000. The generated examples qualitatively exhibited repeated motifs and symmetrical organization also observed in the source corpus. Because no FID, IS, SSIM, Dice, or other formal generative-quality metric, model benchmark, raw-versus-preprocessed ablation, or expert cultural-authenticity assessment was performed, the generative findings are interpreted as proof-of-concept observations rather than quantitative validation. The framework provides an exploratory basis for linking measurable structural characteristics with generative modeling in the computational study of traditional Chinese papercutting.
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BibTeX
@article{ji2026generative,
title = {{A Generative AI Framework for Structural Analysis and DCGAN-Based Synthesis of Traditional Chinese Papercutting Patterns}},
author = {Wenting Ji and Jingyao Chen},
journal = {Mathematical and Computational Applications},
year = {2026},
month = sep,
doi = {10.3390/mca31050188},
url = {https://doi.org/10.3390/mca31050188},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Generative AI Framework for Structural Analysis and DCGAN-Based Synthesis of Traditional Chinese Papercutting Patterns
AU - Wenting Ji
AU - Jingyao Chen
PY - 2026
DA - 2026/09/01
JO - Mathematical and Computational Applications
DO - 10.3390/mca31050188
UR - https://doi.org/10.3390/mca31050188
AB - Traditional Chinese papercutting is an important form of intangible cultural heritage characterized by intricate structures, repeated motifs, and prominent symmetrical organization. Existing digital studies of traditional art commonly emphasize classification, restoration, style transfer, or visual reconstruction, while computational frameworks that combine interpretable structural characterization with generative exploration remain comparatively limited. This study therefore presents an exploratory framework integrating image standardization, quantitative structural-feature analysis, Principal Component Analysis (PCA), and Deep Convolutional Generative Adversarial Network (DCGAN)-based synthesis. The image corpus was standardized by grayscale conversion, Gaussian filtering, Otsu thresholding, morphological processing, resizing, and normalization. Foreground occupancy, edge density, connected-component complexity, and horizontal and vertical symmetry were extracted as interpretable structural descriptors. PCA was applied to the resulting feature matrix to examine variation among samples. The DCGAN used a 100-dimensional latent vector and generated 64 × 64 single-channel outputs; training progression was documented through generator/discriminator loss curves and fixed-latent-vector samples up to epoch 2000. The generated examples qualitatively exhibited repeated motifs and symmetrical organization also observed in the source corpus. Because no FID, IS, SSIM, Dice, or other formal generative-quality metric, model benchmark, raw-versus-preprocessed ablation, or expert cultural-authenticity assessment was performed, the generative findings are interpreted as proof-of-concept observations rather than quantitative validation. The framework provides an exploratory basis for linking measurable structural characteristics with generative modeling in the computational study of traditional Chinese papercutting.
SN - 2297-8747
SN - 1300-686X
ER -