Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.04966 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917887657115648 |
|---|---|
| author | Lin, Yi Gu, Lin Cui, Ziteng Su, Shenghan Hao, Yumo Tian, Yingtao Harada, Tatsuya Yang, Jianfei |
| author_facet | Lin, Yi Gu, Lin Cui, Ziteng Su, Shenghan Hao, Yumo Tian, Yingtao Harada, Tatsuya Yang, Jianfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emergence of Painting Ability via Recognition-Driven Evolution Lin, Yi Gu, Lin Cui, Ziteng Su, Shenghan Hao, Yumo Tian, Yingtao Harada, Tatsuya Yang, Jianfei Computer Vision and Pattern Recognition 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. |
| title | Emergence of Painting Ability via Recognition-Driven Evolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.04966 |