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.
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...
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.
arXiv:2608. 06751v1 Announce Type: cross Abstract: Artist-grounded image generation requires more than appending an artist name to a prompt.
arXiv:2608. 14405v1 Announce Type: cross Abstract: Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation.
arXiv:2605.20941v2 Announce Type: replace Abstract: Existing neural painting methods are target-driven: given a reference image, strokes are optimized to reconstruct it, fixing the outcome before pai...
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arXiv:2607. 17940v1 Announce Type: cross Abstract: This paper frames Generative Artificial Intelligence (AI) not as an unprecedented technological rupture, but as an industrial-scale manifestation of a deeply rooted historical process.
The paper introduces ViralRecipesTrans, a dataset of execution flow graphs from culinary videos linked to specific creators, and proposes a graph learning framework to discover procedural personas. It shows that discrete topological metrics better capture a creator’s workflow than lexical classifiers, and presents a two‑stage generative model that predicts a creator’s exact execution graph for new dishes. The study finds that few‑shot LLMs excel at semantic assignment but lack macro‑planning, while the structured model offers superior topological control, and an ensemble approach combines both strengths for personalized workflow generation.
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.
Sanyu Studio is a multi‑agent dialogue system that treats 321 Sanyu oil paintings as agents equipped with fact, interpretation, organization, and memory‑filtering mechanisms. The paper reports on a seven‑day workshop with eight art‑university participants, showing that user prompts, evidence organization, and cognitive tendencies produced divergent yet coherent digital narratives of Sanyu. The study suggests that, when historical evidence is limited, AI can amplify human agency and provide public audiences with an interactive entry point into art‑historical interpretation.
arXiv:2607. 05841v1 Announce Type: cross Abstract: Structured representation can characterize semantic objects and relationships in images.