Traditional Chinese textile patterns are often used as decorative elements on garments, fabrics, and accessories, adding unique cultural charm and aesthetic value to textile products. Recently, the big advances in data-driven artificial intelligence (AI) technology offer new ways to study textile patterns, but collecting enough data for training is challenging. One-shot image synthesis technology can help in constructing datasets under data-scarce conditions. Nevertheless, existing one-shot image generation methods are inadequate in fulfilling the structural layout and efficiency requirements for the production of traditional Chinese textile patterns. This paper proposes a one-shot Chinese textile pattern image generation (OS-CTPIG) method, which facilitates the generation of Chinese textile patterns with central radial structures, faithful colors, and exquisite details. A multiscale framework is employed for the generation of details in a coarse-to-fine manner. The simultaneous control over color and structure, along with the respective optimization at the color space and spatial feature space, complemented by a color leakage constraint module, is designed to preserve the traditional aesthetic standards in structures. Moreover, the integration of an energy function-enhanced sliced Wasserstein distance optimization objective and a dynamic task-weighting mechanism is proposed to improve computational efficiency. The experimental results demonstrate that the OS-CTPIG method is both effective and efficient in generating traditional Chinese textile pattern images under one-shot conditions. Subsequently, a dataset comprising over 3000 images of traditional Chinese textile pattern images has been constructed successfully.
Shi et al. (Mon,) studied this question.