Abstract
From Paleolithic cave paintings to Impressionism, human painting has evolved to depict increasingly complex and detailed scenes, conveying more nuanced messages. This paper attempts to emerge this artistic capability by simulating the evolutionary pressures that enhance visual communication efficiency. Specifically, we present a model with a stroke branch and a palette branch that together simulate human-like painting. The palette branch learns a limited colour palette, while the stroke branch parameterises each stroke using Bézier curves to render an image, subsequently evaluated by a high-level recognition module. We quantify the efficiency of visual communication by measuring the recognition accuracy achieved with machine vision. The model then optimises the control points and colour choices for each stroke to maximise recognition accuracy with minimal strokes and colours. Experimental results show that our model achieves superior performance in high-level recognition tasks, delivering artistic expression and aesthetic appeal, especially in abstract sketches. Additionally, our approach shows promise as an efficient bit-level image compression technique, outperforming traditional methods.
Abstract (translated)
从旧石器时代的洞穴壁画到印象派，人类绘画的发展历程中，描绘的场景逐渐变得更加复杂和详细，并传达出更为细腻的信息。本文试图通过模拟增强视觉通信效率的进化压力来再现这种艺术能力。具体来说，我们提出了一种具有笔触分支和调色板分支的模型，这些分支共同模拟了类似人类的作画方式。调色板分支学习有限的颜色方案，而笔触分支则使用Bézier曲线参数化每个笔触以生成图像，并随后通过高层次识别模块进行评估。我们通过测量机器视觉实现的识别准确率来量化视觉通信的有效性。模型优化每条笔触的控制点和颜色选择，以在最少的笔画和颜色下最大化识别准确性。 实验结果显示，我们的模型在高级别识别任务中表现出色，在抽象草图方面尤其具有艺术表现力和美学吸引力。此外，我们的方法显示出了作为高效位级图像压缩技术的巨大潜力，并且优于传统的方法。
URL
https://arxiv.org/abs/2501.04966
