Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's global style. Conventional style transfer methods transfer the style of one or a few reference artworks to a content image in a One-to-One manner, making them effective for artwork-level stylization but limited in representing the broader stylistic distribution of an artist.
arXiv:2608. 06751v1 Announce Type: cross Abstract: Artist-grounded image generation requires more than appending an artist name to a prompt.
By Kuan Xing, Ye Wang, Changyi Gan, Yuheng Li, Thao Nguyen, Yi Chang, Yilin Wang
The aim of this paper is twofold. First, it investigates whether newer generative models are getting better at pastiching contemporary artworks.
arXiv:2609.13352v1 Announce Type: cross
Abstract: We present three robotic art installations which explore the aesthetics of adaptive behavior. Through embodied machine leaning and digital evolution,...
By Sofian Audry, Stephen Kelly
Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent generative AI systems can synthesize artworks with high fidelity, they primarily model distributions over finished artifacts rather than the creative processes underlying their creation.
arXiv:2607. 08331v1 Announce Type: cross Abstract: Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production.
By Kaustubh Kumar, Ashutosh Ranjan, Vivek Srivastava, Blessin Varkey, Shirish Karande
arXiv:2607.
By Ahmed M. Abuzuraiq, Philippe Pasquier
arXiv:2608.29644v1 Announce Type: cross
Abstract: Attributing an artwork to an artist has traditionally relied on detailed visual observations and descriptions, known as stylistic analysis in art his...
By Marc S. Walton, Astrid Harth
arXiv:2602.08059v2 Announce Type: replace-cross
Abstract: Text-to-image diffusion models can reproduce specific artists visual styles at extremely low cost, raising copyright and deployment safety co...
By Tong Zhang, Ru Zhang, Jianyi Liu
arXiv:2607. 23126v1 Announce Type: cross Abstract: Generative AI design tools make natural-language prompts a starting point for design, placing new articulation demands on designers.
By Daisaku Sato
arXiv:2608. 05026v1 Announce Type: cross Abstract: High-quality annotation of artworks is essential for computational art research, yet extracting implicit semantics remains challenging due to the reliance on culturally grounded meanings and deep contextual knowledge behind the images.
By Xiaoyan Gu, Yifang Wang, Wenqing Zheng, Haozhong Liu, Yixia Zheng, Peiyi Jiang, Wenjie Ning, Wei Zhang, Wei Chen
Generative AI tools for creative work tend to be designed around the goal of removing friction, on the assumption that smoother iteration and faster output translate into more value for the designer. We argue, however, that this framing leaves out something important about how design ideation works, namely reflection-in-action.