通过笔触聚类还原艺术创作过程，揭示从草图到完成的演变路径。
Sketch & Paint: Stroke-by-Stroke Evolution of Visual Artworks
- 基于邻近性聚类将像素图转为矢量笔触，推断创作顺序。
- 在WikiArt数据上验证可生成合理笔触序列，支持多种图像类型。
- 适合艺术研究、交互展示与创作教学场景。
理解基于笔触的艺术品演化过程，有助于推进艺术品学习、欣赏与交互展示。尽管知名艺术品的笔触顺序大多未知，但对近自然图像绘制过程中的笔触序列进行建模，能显著提升对艺术技法的理解。本文提出一种基于邻近性聚类的新方法，将像素图像通过参数曲线转换为矢量图像，并利用聚类方法确定提取笔触的顺序。所提算法展示了推断未知艺术品笔触序列的潜力。我们在WikiArt数据集上评估了该方法性能，并定性展示了合理的笔触序列。此外，该方法对线稿、人物素描、绘画及照片等多种输入图像类型均表现出鲁棒性。通过探索笔触提取与序列构建，旨在深化对艺术创作技法复杂性的理解，还原从初稿到终稿的逐步重构过程，从而丰富对艺术创作历程的认知。
原文摘要 · Abstract (English)
Understanding the stroke-based evolution of visual artworks is useful for advancing artwork learning, appreciation, and interactive display. While the stroke sequence of renowned artworks remains largely unknown, formulating this sequence for near-natural image drawing processes can significantly enhance our understanding of artistic techniques. This paper introduces a novel method for approximating artwork stroke evolution through a proximity-based clustering mechanism. We first convert pixel images into vector images via parametric curves and then explore the clustering approach to determine the sequence order of extracted strokes. Our proposed algorithm demonstrates the potential to infer stroke sequences in unknown artworks. We evaluate the performance of our method using WikiArt data and qualitatively demonstrate the plausible stroke sequences. Additionally, we demonstrate the robustness of our approach to handle a wide variety of input image types such as line art, face sketches, paintings, and photographic images. By exploring stroke extraction and sequence construction, we aim to improve our understanding of the intricacies of the art development techniques and the step-by-step reconstruction process behind visual artworks, thereby enriching our understanding of the creative journey from the initial sketch to the final artwork.
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