Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings

• URL: http://arxiv.org/abs/2511.12880v2
• Date: Thu, 20 Nov 2025 02:10:22 GMT
• Title: Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings
• Authors: Zihao Lin, Zhenshan Shi, Sasa Zhao, Hanwei Zhu, Lingyu Zhu, Baoliang Chen, Lei Mo,
• Abstract summary: We propose a data-driven framework for automatic and interpretable creativity assessment from drawings.<n>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.
• Score: 18.09092203643732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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