# 超越星月夜：面向艺术家风格图像生成的捷径感知控制状态规划框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-07 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmspbms6h0bo8rortimxsvmcy
- 原文链接：https://arxiv.org/abs/2608.06751

## AI 摘要

Atelier框架将模糊的艺术意图转化为显式控制状态，分离场景锚点、保留/变换决策与风格假设，借助艺术家级知识和局部补丁参考迭代优化，减少模型对艺术家名的套路化响应。配套ArtIntentBench基准覆盖梵高与齐白石的多项评测。在开源与闭源生成器上，Atelier相比提示工程、检索增强及通用智能体基线，显著提升风格保真度并减少捷径替换。

## 正文

Artist-grounded image generation requires more than appending an artist name to a prompt. Image models often respond to artist names through canonical shortcuts, such as recurring motifs, generic palettes, or overrepresented period signatures, rather than preserving the user's intended scene. We introduce Atelier, a shortcut-aware control-state planning framework for artist-grounded image generation. Atelier translates underspecified artistic intent into an explicit control state that separates scene anchors, preserve/transform decisions, style-regime hypotheses, role-bound artist evidence, and shortcut-avoidance constraints. It grounds this state using artist-level knowledge and local patch references, compiles backend-aware generation plans, and iteratively refines candidates through global and local authenticity feedback. We further introduce ArtIntentBench, a benchmark covering Van Gogh and Qi Baishi across artwork re-rendering, period/style-controlled generation, historically unseen subjects, shortcut auditing, and human preference evaluation. Across open-weight and closed-source generators, Atelier improves artist-level style fidelity, preserves source structure more faithfully, and substantially reduces shortcut substitution compared with prompt-engineered, retrieval-augmented, and general-purpose agent baselines. These results suggest that artist-grounded generation is bottlenecked not only by image synthesis, but by the upstream inference of explicit, evidence-grounded artistic controls.