arXiv 2511.128802025-11-20cs.CV

简单线条，大想法：迈向从绘画中可解释的创造力评估

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

Department of Computer Science, South China Normal University, Guang Zhou, China（华南师范大学计算机学院）；Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China（香港城市大学计算机学院）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）

Zihao Lin, Zhenshan Shi, Sasa Zhao, Hanwei Zhu, Lingyu Zhu, Baoliang Chen, Lei Mo

备注：: We updated the version, expanding related work (acknowledging Nath et al., 2025, Pencils to Pixels: A Systematic Study of Creative Drawings) and clarifying how our model builds upon the content-style framework

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AI总结：

本文提出一种基于数据驱动的框架，通过分析绘画内容和风格来自动评估创造力，并提供可解释的可视化结果。

摘要：

通过视觉输出（如绘画）评估人类创造力，在心理学、教育和认知科学等领域发挥着关键作用。然而，当前的评估实践仍然严重依赖专家基于主观评分，这既耗费人力，又具有内在主观性。在本文中，我们提出了一种数据驱动的框架，用于自动且可解释地从绘画中评估创造力。受[6]提出的认知证据启发，即创造力可以来自所画内容（内容）和如何画（风格），我们将创造力评分重新解释为这两个互补维度的函数。具体而言，我们首先在现有创造力标注数据集上添加额外注释，目标是内容类别。基于丰富后的数据集，我们进一步提出一个条件模型，同时预测内容、风格和评分。特别是，条件学习机制使模型能够通过动态调整其视觉特征提取来适应创造力相关的信号，这些信号基于绘画的风格和语义提示。实验结果表明，我们的模型在与现有回归方法相比时达到了最先进的性能，并提供了可解释的可视化，与人类判断一致。代码和注释将在https://github.com/WonderOfU9/CSCA_PRCV_2025公开。

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。