用图文混合控制生成自然中文书法，支持快速学新风格。
CalliffusionV2: Personalized Natural Calligraphy Generation with Flexible Multi-modal Control
- 结合图像与自然语言实现细粒度书法风格控制
- 仅需少量样本即可学习新书法风格，支持非中文字符生成
- 生成结果既符合风格又易被机器和人识别
本文提出CalliffusionV2，一种可灵活控制的中文书法生成系统。该系统融合图像与自然语言输入，在细粒度层面引导书法生成，突破了以往仅依赖单一输入的局限。通过少样本学习，系统能快速掌握新书法风格，并可在未训练的情况下生成非中文字符。大量实验表明，生成的书法作品在风格准确性、神经网络分类器识别率及人工评估中均表现优异，具备高度可辨识性与艺术性。
原文摘要 · Abstract (English)
In this paper, we introduce CalliffusionV2, a novel system designed to produce natural Chinese calligraphy with flexible multi-modal control. Unlike previous approaches that rely solely on image or text inputs and lack fine-grained control, our system leverages both images to guide generations at fine-grained levels and natural language texts to describe the features of generations. CalliffusionV2 excels at creating a broad range of characters and can quickly learn new styles through a few-shot learning approach. It is also capable of generating non-Chinese characters without prior training. Comprehensive tests confirm that our system produces calligraphy that is both stylistically accurate and recognizable by neural network classifiers and human evaluators.
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