洛米斯畫家：重構繪畫過程

摘要

分步驟繪畫教程對於學習藝術技法至關重要，但現有影片資源（如YouTube）缺乏互動性與個性化。儘管近期生成模型在藝術圖像合成方面取得進展，但其難以在不同媒介間泛化，且常出現時間或結構上的不一致性，阻礙了對人類創作流程的真實重現。為此，我們提出一個統一框架，通過語義驅動的風格控制機制實現多媒介繪畫過程生成：將多種媒介嵌入擴散模型的條件空間，並採用跨媒介風格增強技術。該方法能實現跨風格的紋理一致性演變與過程遷移。反向繪畫訓練策略進一步確保生成過程流暢且符合人類創作規律。我們還構建了大型真實繪畫過程數據集，並在跨媒介一致性、時間連貫性與最終圖像保真度方面進行評估，於LPIPS、DINO和CLIP指標上取得優異結果。最後，我們提出的感知距離輪廓（PDP）曲線定量建模創作序列（構圖、色塊鋪陳與細節精修），精準對應人類藝術創作進程。

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.

洛米斯畫家：重構繪畫過程

Loomis Painter: Reconstructing the Painting Process

摘要

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