# PaintFlow: A Unified Framework for Interactive Oil Paintings Editing and Generation Source: [PaintFlow: A Unified Framework for Interactive Oil Paintings Editing and Generation]() Collection index: [llms.txt]() Data updated: 2025-12-11T05:01:04.487Z Authors: Zhangli Hu, Ye Chen, Jiajun Yao, Bingbing Ni Published: 2025-12-09T12:31:00.000Z ## Abstract Oil painting, as a high-level medium that blends human abstract thinking with artistic expression, poses substantial challenges for digital generation and editing due to its intricate brushstroke dynamics and stylized characteristics. Existing generation and editing techniques are often constrained by the distribution of training data and primarily focus on modifying real photographs. In this work, we introduce a unified multimodal framework for oil painting generation and editing. The proposed system allows users to incorporate reference images for precise semantic control, hand-drawn sketches for spatial structure alignment, and natural language prompts for high-level semantic guidance, while consistently maintaining a unified painting style across all outputs. Our method achieves interactive oil painting creation through three crucial technical advancements. First, we enhance the training stage with spatial alignment and semantic enhancement conditioning strategy, which map masks and sketches into spatial constraints, and encode contextual embedding from reference images and text into feature constraints, enabling object-level semantic alignment. Second, to overcome data scarcity, we propose a self-supervised style transfer pipeline based on Stroke-Based Rendering (SBR), which simulates the inpainting dynamics of oil painting restoration, converting real images into stylized oil paintings with preserved brushstroke textures to construct a large-scale paired training dataset. Finally, during inference, we integrate features using the AdaIN operator to ensure stylistic consistency. Extensive experiments demonstrate that our interactive system enables fine-grained editing while preserving the artistic qualities of oil paintings, achieving an unprecedented level of imagination realization in stylized oil paintings generation and editing. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multimodal Oil Painting Generation on DiffusionDB stylized](): Multimodal Oil Painting Generation; DiffusionDB stylized; Gram Similarity: 0.619. - [Image Editing on DiffusionDB User Study (test)](): Image Editing; DiffusionDB User Study (test); Semantic Alignment Score: 1.57. ## Original sources and implementation [Original paper]() [PDF]()