Loomis Painter: Reconstructing the Painting Process
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.
69240abab5612535ed955a34
Markus Pobitzer
claimed_verified
2025-11-24T12:26:53.036Z
677d4c16da00f8f3698a501d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/V_QVrZRgIhTZSz9eCG0Om.png
Markus
Markus-Pobitzer
user
Markus-Pobitzer
69240abab5612535ed955a35
Chang Liu
69240abab5612535ed955a36
Chenyi Zhuang
69240abab5612535ed955a37
Teng Long
69240abab5612535ed955a38
Bin Ren
69240abab5612535ed955a39
Nicu Sebe
Loomis Painter: Reconstructing the Painting Process
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.
A unified framework using diffusion models with semantic control and cross-medium style augmentation generates consistent and high-fidelity multi-media painting processes, supported by a comprehensive dataset and evaluation metrics.
diffusion models semantics-driven style control cross-medium style augmentation texture evolution process transfer reverse-painting training strategy LPIPS DINO CLIP metrics Perceptual Distance Profile (PDP)