PPJudge: Towards Human-Aligned Assessment of Artistic Painting Process
This work addresses a gap in computational creativity and art education by enabling dynamic assessment of painting processes, though it is incremental as it builds on existing image assessment methods.
The paper tackles the problem of assessing artistic painting processes, which existing methods overlook by focusing only on static final images, and proposes a novel framework that outperforms baselines in accuracy, robustness, and alignment with human judgment.
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.