{"ID":2840297,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12880","arxiv_id":"2511.12880","title":"Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings","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","short_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...","url_abs":"https://arxiv.org/abs/2511.12880","url_pdf":"https://arxiv.org/pdf/2511.12880v2","authors":"[\"Zihao Lin\",\"Zhenshan Shi\",\"Sasa Zhao\",\"Hanwei Zhu\",\"Lingyu Zhu\",\"Baoliang Chen\",\"Lei Mo\"]","published":"2025-11-17T02:16:01Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":606956,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840297,"paper_url":"https://arxiv.org/abs/2511.12880","paper_title":"Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings","repo_url":"https://github.com/WonderOfU9/CSCA_PRCV_2025","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}