随着市场对个性化服装需求的增长,传统服装设计流程面临创新费时和效率低的挑战。为此,本文提出了一种基于风格编辑的智能服饰样式生成方法,通过整合深度学习技术来优化服饰设计流程。首先,利用网络爬虫技术构建多样化风格的服饰图像数...随着市场对个性化服装需求的增长,传统服装设计流程面临创新费时和效率低的挑战。为此,本文提出了一种基于风格编辑的智能服饰样式生成方法,通过整合深度学习技术来优化服饰设计流程。首先,利用网络爬虫技术构建多样化风格的服饰图像数据集,并进行去噪及归一化处理以提高数据质量。接着,对Style GAN模型进行参数微调,使其学习目标数据集中的服饰样式和风格,以减少生成图像与目标风格之间的偏差。通过对Style GAN模型潜在空间的主成分分析,提取关键的服饰风格语义特征,并利用这些特征向量,实现在多个维度上的服饰风格控制,包括款式、图案明暗、轮廓及颜色等。在此基础上,将调整后的潜向量映射到图像空间后,生成具有不同风格的服饰图像。最后,实验结果验证了本文所提的智能服饰样式生成方法的有效性。As the market demand for personalized fashion grows, traditional clothing design processes face challenges with time-consuming innovation and low efficiency. To address this, this paper introduces a fashion style generation method based on style editing, integrating deep learning technologies to optimize the fashion design process. Firstly, a diverse style fashion image dataset was constructed using web scraping techniques, followed by denoising and normalization to enhance data quality. Subsequently, the Style GAN model was fine-tuned to learn the fashion styles and characteristics from the target dataset, minimizing the discrepancies between generated images and target styles. Principal component analysis of the latent space within the Style GAN model was conducted to extract key semantic features of fashion styles. Utilizing these feature vectors, control over various dimensions of fashion style was achieved, including style, pattern brightness, contour, and color. Upon adjusting the latent vectors, they were mapped back to the image space, generating fashion images with varied styles. Finally, experimental results confirmed the effectiveness of the proposed fashion style generation method.展开更多
文摘随着市场对个性化服装需求的增长,传统服装设计流程面临创新费时和效率低的挑战。为此,本文提出了一种基于风格编辑的智能服饰样式生成方法,通过整合深度学习技术来优化服饰设计流程。首先,利用网络爬虫技术构建多样化风格的服饰图像数据集,并进行去噪及归一化处理以提高数据质量。接着,对Style GAN模型进行参数微调,使其学习目标数据集中的服饰样式和风格,以减少生成图像与目标风格之间的偏差。通过对Style GAN模型潜在空间的主成分分析,提取关键的服饰风格语义特征,并利用这些特征向量,实现在多个维度上的服饰风格控制,包括款式、图案明暗、轮廓及颜色等。在此基础上,将调整后的潜向量映射到图像空间后,生成具有不同风格的服饰图像。最后,实验结果验证了本文所提的智能服饰样式生成方法的有效性。As the market demand for personalized fashion grows, traditional clothing design processes face challenges with time-consuming innovation and low efficiency. To address this, this paper introduces a fashion style generation method based on style editing, integrating deep learning technologies to optimize the fashion design process. Firstly, a diverse style fashion image dataset was constructed using web scraping techniques, followed by denoising and normalization to enhance data quality. Subsequently, the Style GAN model was fine-tuned to learn the fashion styles and characteristics from the target dataset, minimizing the discrepancies between generated images and target styles. Principal component analysis of the latent space within the Style GAN model was conducted to extract key semantic features of fashion styles. Utilizing these feature vectors, control over various dimensions of fashion style was achieved, including style, pattern brightness, contour, and color. Upon adjusting the latent vectors, they were mapped back to the image space, generating fashion images with varied styles. Finally, experimental results confirmed the effectiveness of the proposed fashion style generation method.
