基于深度生成对抗网络的机器人书法风格生成
Style Generation in Robot Calligraphy with Deep Generative Adversarial Networks
AI总结：
针对汉字数量多导致风格统一书法字体生成难、现有方法因数据等不足生成质量低的问题，提出基于deepGAN的专业级书法生成模型，通过高质量数据集构建与专业图灵测试验证，达到当前书法生成方法的最优水平。
摘要：
机器人书法是人工智能在艺术与教育领域的新兴探索方向。传统书法生成研究主要聚焦于基于工具的图像处理、生成模型、风格迁移等方法。与英文字母不同，汉字数量达数万之多，这导致生成一套包含6000余个字符的风格统一的中国书法字体存在难度。由于缺乏高质量数据集、书法知识的正式定义以及科学的艺术评估方法，生成结果往往质量较低，达不到专业级要求。为解决上述问题，本文提出一种基于深度生成对抗网络（deepGAN）的自动书法生成模型，可生成具备专业水准的风格书法字体。该方法的核心亮点包括：（1）数据集采用高精度书法合成方法，确保其高质量与充足数量；（2）邀请专业书法家开展一系列图灵测试，评估模型生成结果与人类艺术水平的差距；（3）实验结果表明，所提模型在当前书法生成方法中处于最优水平（state-of-the-art）。图灵测试与相似度评估验证了该方法的有效性。
英文摘要：
Robot calligraphy is an emerging exploration of artificial intelligence in the fields of art and education. Traditional calligraphy generation researches mainly focus on methods such as tool-based image processing, generative models, and style transfer. Unlike the English alphabet, the number of Chinese characters is tens of thousands, which leads to difficulties in the generation of a style consistent Chinese calligraphic font with over 6000 characters. Due to the lack of high-quality data sets, formal definitions of calligraphy knowledge, and scientific art evaluation methods, The results generated are frequently of low quality and falls short of professional-level requirements. To address the above problem, this paper proposes an automatic calligraphy generation model based on deep generative adversarial networks (deepGAN) that can generate style calligraphy fonts with professional standards. The key highlights of the proposed method include: (1) The datasets use a high-precision calligraphy synthesis method to ensure its high quality and sufficient quantity; (2) Professional calligraphers are invited to conduct a series of Turing tests to evaluate the gap between model generation results and human artistic level; (3) Experimental results indicate that the proposed model is the state-of-the-art among current calligraphy generation methods. The Turing tests and similarity evaluations validate the effectiveness of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
