洛米斯画家：重构绘画过程

摘要

分步绘画教程对于学习艺术技法至关重要，但现有视频资源（如YouTube）缺乏互动性与个性化。尽管近期生成模型在艺术图像合成方面取得进展，但其难以跨媒介泛化，且常出现时序或结构不一致的问题，阻碍了对人类创作流程的忠实复现。为此，我们提出一个统一的多媒介绘画过程生成框架，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，并采用跨媒介风格增强技术。该方法能实现跨风格的纹理一致性演变与过程迁移，结合逆向绘画训练策略进一步确保生成过程流畅且符合人类创作逻辑。我们还构建了大规模真实绘画过程数据集，从跨媒介一致性、时序连贯性和最终图像保真度三个维度进行评估，在LPIPS、DINO和CLIP指标上取得优异结果。最后，我们提出的感知距离轮廓（PDP）曲线量化模拟了构图、色块铺陈与细节精修等创作序列，精准对应人类艺术创作进程。

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.

洛米斯画家：重构绘画过程

Loomis Painter: Reconstructing the Painting Process

摘要

Support