Step-by-step painting tutorials are used for learning artistic techniques, but existing video resources lack interactivity and personalization. While recent generative models have advanced artistic image synthesis, they still struggle to generate temporally and structurally consistent videos. Video models excel in consistent frame generation, yet fine-tuning an image-to-video model on forward painting sequences creates a long-range dependency on the first-frame reference, disrupting its pretrained attention pattern and hindering reconstruction. We address this issue by training on reversed sequences that better preserve the reference-attention behavior. Building on this, we present Loomis Painter, a framework for reconstructing coherent painting processes. We further introduce a media-transfer training strategy that decouples input appearance from the target process, enabling generation across different artistic media. Trained on a curated dataset of real-world painting processes, our method outperforms existing baselines across LPIPS, DINO, and CLIP metrics and is consistently preferred in expert and non-expert user studies. Code available at https://github.com/Markus-Pobitzer/wlp.

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