无需训练即可生成风格精准的草图，保持内容结构与细节一致。
Stroke2Sketch: Harnessing Stroke Attributes for Training-Free Sketch Generation
- 通过跨图像笔画注意力实现精细风格迁移
- 在不损失语义结构的前提下精准控制线条粗细与纹理
- 适合需要快速生成高质量手绘风格草图的设计师或研究人员
基于参考风格生成草图需精确传递笔画属性（如线宽、形变、纹理稀疏性），同时保持语义结构和内容忠实度。为此，我们提出Stroke2Sketch——一种无需训练的框架，引入跨图像笔画注意力机制，嵌入自注意力层以建立细粒度语义对应关系，实现准确的笔画属性迁移。该方法可自适应地将参考笔画特征融合到内容图像中，同时保持结构完整性。此外，我们设计了自适应对比度增强与语义聚焦注意力，强化内容保留与前景强调。Stroke2Sketch能生成高度贴近手工绘制结果的风格化草图，在笔画表现力与语义连贯性上优于现有方法。代码已开源：https://github.com/rane7/Stroke2Sketch。
原文摘要 · Abstract (English)
Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within self-attention layers to establish fine-grained semantic correspondences and enable accurate stroke attribute transfer. This allows our method to adaptively integrate reference stroke characteristics into content images while maintaining structural integrity. Additionally, we develop adaptive contrast enhancement and semantic-focused attention to reinforce content preservation and foreground emphasis. Stroke2Sketch effectively synthesizes stylistically faithful sketches that closely resemble handcrafted results, outperforming existing methods in expressive stroke control and semantic coherence. Codes are available at https://github.com/rane7/Stroke2Sketch.
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