ArtPeer: Artistic Alignment via Reflection-based Prompt Evolution with Simulated Artist

Kaustubh Kumar, Ashutosh Ranjan, Vivek Srivastava, Shirish Karande

Track: Paper

Keywords: art personalisation, generative models, visual art, image generation, reflection-based learning, style transfer

Abstract: Art inspiration, whether from a specific artist, artwork, or movement, is a common anchor in text to image generation for artistic alignment. Although recent advances in generative models have led to impressive visual output, they often lack grounding in artistic intent, context, persona, and evolving self-reflection that are hallmarks of human creativity. Their output may drift from the intended artistic and symbolic logic present in the art inspiration due to prompt ambiguity, latent biases, or model hallucinations. We present ArtPeer, a framework for robust artistic alignment that places reflection-guided prompt evolution inside the generation loop. ArtPeer builds simulated artist persona from biographical, stylistic and iconographic knowledge, enabling them to act as domain-specific critics. In a Socratic reflection dialogue, the persona-aligned artist agent questions a generation agent, identifies deviations, and iteratively refines the prompt until both stylistic and conceptual alignment is achieved. We validate the effectiveness of the proposed framework through qualitative and quantitative evaluation in challenging settings. ArtPeer robustly aligns art that is more meaningful and contextually relevant than existing state-of-the-art methods.

Submission Number: 193