让AI学会用绘图指令生成艺术作品，可解释且分辨率无关。
Learning to Synthesize Graphics Programs for Geometric Artworks
- 将绘图工具视为可执行程序，预测作画步骤序列。
- 能还原复杂图像，生成高质量可运行代码。
- 适合需要可解释性与可复现性的艺术生成场景。
创作与理解艺术长久以来被视为人类的标志性能力。面对完成的数字艺术作品，专业图形艺术家能直观分解并使用各种绘图工具（如线条工具、油漆桶、图层功能及不透明度、混合模式等）进行复现。尽管近期研究多集中于艺术生成，但通常将作品视为最终图像。为弥合像素级结果与实际绘制过程之间的差距，我们提出一种方法，将一组绘图工具视为可执行程序，通过预测一系列操作步骤来实现最终图像，从而支持可理解、分辨率无关的复现。实验表明，我们的程序合成器 Art2Prog 能全面理解复杂输入图像，并利用高质量可执行程序进行还原。结果验证了机器从图像中捕捉高层信息并生成紧凑程序描述的潜力。
原文摘要 · Abstract (English)
Creating and understanding art has long been a hallmark of human ability. When presented with finished digital artwork, professional graphic artists can intuitively deconstruct and replicate it using various drawing tools, such as the line tool, paint bucket, and layer features, including opacity and blending modes. While most recent research in this field has focused on art generation, proposing a range of methods, these often rely on the concept of artwork being represented as a final image. To bridge the gap between pixel-level results and the actual drawing process, we present an approach that treats a set of drawing tools as executable programs. This method predicts a sequence of steps to achieve the final image, allowing for understandable and resolution-independent reproductions under the usage of a set of drawing commands. Our experiments demonstrate that our program synthesizer, Art2Prog, can comprehensively understand complex input images and reproduce them using high-quality executable programs. The experimental results evidence the potential of machines to grasp higher-level information from images and generate compact program-level descriptions.
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