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 B\'ezier 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.

本研究探讨了绘画能力的演变，特别是如何通过模拟促进视觉交流效率的演化压力来实现这一能力。我们提出了一种模型，它通过学习有限的颜色调色板和贝塞尔曲线参数化笔画，优化视觉识别的准确性，并在高层次识别任务中表现出优越性能，本研究还展示了其作为高效图像压缩技术的潜力。

通过识别驱动的演化实现绘画能力的出现