Tracking the Progress of Generative Models in Artistic Style Imitation

Replicating the style of historical artistic traditions remains a notoriously challenging task for vision-based generative models, as artistic style encodes complex visual, cultural, and contextual dimensions that extend beyond surface-level appearance. Despite rapid advances in generative modeling, it remains unclear to what extent recent systems have achieved substantive improvements in stylistic emulation across diverse historical traditions. This article presents a systematic evaluation of contemporary generative models in artistic style imitation, comparing their outputs to those of earlier model generations. The study builds upon the AI-Pastiche dataset by incorporating six additional recent state-of-the-art models, thereby enabling a longitudinal assessment of performance progression over time. To ensure methodological consistency and comparability, the evaluation adopts the same experimental protocol used in the original release of the dataset. The analysis combines controlled prompt-based image generation with extensive user studies designed to assess multiple dimensions of aesthetic and stylistic performance. These dimensions include: (i) visual plausibility and perceptual realism; (ii) stylistic coherence and fidelity to historical conventions; (iii) versatility across a broad range of artistic traditions; (iv) adherence to prompt-specified content and stylistic constraints; and (v) the absence of structural artifacts or semantic inconsistencies. The results indicate substantial and measurable improvements in stylistic emulation in recent models. Nevertheless, persistent limitations remain, particularly in the consistent reproduction of deeper compositional structures and culturally embedded stylistic features. These findings suggest that progress in surface-level resemblance does not necessarily equate to structural or contextual understanding.