Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917093526470656 |
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| author | Lin, Zihao Shi, Zhenshan Zhao, Sasa Zhu, Hanwei Zhu, Lingyu Chen, Baoliang Mo, Lei |
| author_facet | Lin, Zihao Shi, Zhenshan Zhao, Sasa Zhu, Hanwei Zhu, Lingyu Chen, Baoliang Mo, Lei |
| contents | Assessing human creativity through visual outputs, such as drawings, plays a critical role in fields including psychology, education, and cognitive science. However, current assessment practices still rely heavily on expert-based subjective scoring, which is both labor-intensive and inherently subjective. In this paper, we propose a data-driven framework for automatic and interpretable creativity assessment from drawings. Motivated by the cognitive evidence proposed in [6] that creativity can emerge from both what is drawn (content) and how it is drawn (style), we reinterpret the creativity score as a function of these two complementary dimensions. Specifically, we first augment an existing creativity-labeled dataset with additional annotations targeting content categories. Based on the enriched dataset, we further propose a conditional model predicting content, style, and ratings simultaneously. In particular, the conditional learning mechanism that enables the model to adapt its visual feature extraction by dynamically tuning it to creativity-relevant signals conditioned on the drawing's stylistic and semantic cues. Experimental results demonstrate that our model achieves state-of-the-art performance compared to existing regression-based approaches and offers interpretable visualizations that align well with human judgments. The code and annotations will be made publicly available at https://github.com/WonderOfU9/CSCA_PRCV_2025 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_12880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings Lin, Zihao Shi, Zhenshan Zhao, Sasa Zhu, Hanwei Zhu, Lingyu Chen, Baoliang Mo, Lei Computer Vision and Pattern Recognition Assessing human creativity through visual outputs, such as drawings, plays a critical role in fields including psychology, education, and cognitive science. However, current assessment practices still rely heavily on expert-based subjective scoring, which is both labor-intensive and inherently subjective. In this paper, we propose a data-driven framework for automatic and interpretable creativity assessment from drawings. Motivated by the cognitive evidence proposed in [6] that creativity can emerge from both what is drawn (content) and how it is drawn (style), we reinterpret the creativity score as a function of these two complementary dimensions. Specifically, we first augment an existing creativity-labeled dataset with additional annotations targeting content categories. Based on the enriched dataset, we further propose a conditional model predicting content, style, and ratings simultaneously. In particular, the conditional learning mechanism that enables the model to adapt its visual feature extraction by dynamically tuning it to creativity-relevant signals conditioned on the drawing's stylistic and semantic cues. Experimental results demonstrate that our model achieves state-of-the-art performance compared to existing regression-based approaches and offers interpretable visualizations that align well with human judgments. The code and annotations will be made publicly available at https://github.com/WonderOfU9/CSCA_PRCV_2025 |
| title | Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.12880 |