让AI生成连贯的绘画过程，还原真实创作步骤。
Loomis Painter: Reconstructing the Painting Process
- 用语义驱动风格控制，统一多媒介绘画生成。
- 训练策略确保生成过程平滑自然，符合人类创作习惯。
- 可量化分析创作阶段，适合艺术教育与创作研究。
分步绘画教程对学习艺术技巧至关重要，但现有视频资源（如YouTube）缺乏交互性与个性化。尽管生成模型在图像合成方面取得进展，却难以跨媒介泛化，常出现时间或结构不一致问题，无法忠实还原人类创作流程。为此，我们提出一个统一框架，通过语义驱动的风格控制机制，将多种媒介嵌入扩散模型的条件空间，并利用跨媒介风格增强，实现纹理演变的一致性与流程迁移。逆向绘画训练策略进一步保证生成过程的流畅性与人类对齐。我们还构建了一个大规模真实绘画过程数据集，评估跨媒介一致性、时间连贯性及最终图像保真度，在LPIPS、DINO和CLIP指标上表现优异。最后，提出的感知距离曲线（PDP）定量刻画创作序列——构图、色块填充、细节精修——反映人类艺术发展路径。
原文摘要 · Abstract (English)
Step-by-step painting tutorials are vital for learning artistic techniques, but existing video resources (e.g., YouTube) lack interactivity and personalization. While recent generative models have advanced artistic image synthesis, they struggle to generalize across media and often show temporal or structural inconsistencies, hindering faithful reproduction of human creative workflows. To address this, we propose a unified framework for multi-media painting process generation with a semantics-driven style control mechanism that embeds multiple media into a diffusion models conditional space and uses cross-medium style augmentation. This enables consistent texture evolution and process transfer across styles. A reverse-painting training strategy further ensures smooth, human-aligned generation. We also build a large-scale dataset of real painting processes and evaluate cross-media consistency, temporal coherence, and final-image fidelity, achieving strong results on LPIPS, DINO, and CLIP metrics. Finally, our Perceptual Distance Profile (PDP) curve quantitatively models the creative sequence, i.e., composition, color blocking, and detail refinement, mirroring human artistic progression.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。