首个面向绘画过程的动态评估框架，更贴近人类审美判断。
PPJudge: Towards Human-Aligned Assessment of Artistic Painting Process
- 构建首个大规模绘画过程数据集PPAD，含8项专家标注属性。
- 提出基于Transformer的PPJudge模型，时序感知+混合专家架构提升评估效果。
- 适用于艺术创作分析、智能美育系统开发，助力计算创意研究。
艺术图像评估已成为计算机视觉中的重要研究方向。近年来，大量数据集和方法被用于评估绘画作品的美学质量，但现有方法大多仅关注静态最终图像，忽略了绘画过程的动态性与多阶段特征。为填补这一空白，本文提出一种面向人类审美的绘画过程评估框架。具体而言，我们构建了首个大规模绘画过程评估数据集PPAD，包含真实与合成的绘画过程图像，由领域专家标注八项详细属性。同时，提出PPJudge（绘画过程评估模型），一个基于Transformer的模型，融合时序感知位置编码与异构专家混合架构，有效实现对绘画过程的综合评估。实验表明，该方法在准确性、鲁棒性及与人类判断的一致性方面均优于现有基线，为计算创造力与艺术教育研究提供了新视角。
原文摘要 · Abstract (English)
Artistic image assessment has become a prominent research area in computer vision. In recent years, the field has witnessed a proliferation of datasets and methods designed to evaluate the aesthetic quality of paintings. However, most existing approaches focus solely on static final images, overlooking the dynamic and multi-stage nature of the artistic painting process. To address this gap, we propose a novel framework for human-aligned assessment of painting processes. Specifically, we introduce the Painting Process Assessment Dataset (PPAD), the first large-scale dataset comprising real and synthetic painting process images, annotated by domain experts across eight detailed attributes. Furthermore, we present PPJudge (Painting Process Judge), a Transformer-based model enhanced with temporally-aware positional encoding and a heterogeneous mixture-of-experts architecture, enabling effective assessment of the painting process. Experimental results demonstrate that our method outperforms existing baselines in accuracy, robustness, and alignment with human judgment, offering new insights into computational creativity and art education.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。