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

TL;DR

This paper introduces a data-driven, interpretable framework for assessing human creativity from drawings, leveraging content and style analysis to outperform existing methods and align with human judgments.

Contribution

It proposes a novel conditional model that predicts content, style, and creativity ratings simultaneously, enhancing interpretability and performance in creativity assessment from drawings.

Findings

Achieves state-of-the-art performance on creativity prediction

Provides interpretable visualizations aligned with human judgments

Enriches dataset with content annotations for better analysis

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