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.
Abstract (translated)
油画作为一种将人类抽象思维与艺术表达相结合的高级媒介，由于其复杂的笔触动态和风格化特征，在数字生成和编辑方面面临着重大挑战。现有的生成和编辑技术通常受到训练数据分布的限制，并且主要侧重于修改真实照片。在这项工作中，我们引入了一个统一的多模态框架，用于油画的生成和编辑。该系统允许用户通过参考图像进行精确语义控制、手绘草图进行空间结构对齐以及自然语言提示进行高层次语义指导的同时，在所有输出中保持一致的绘画风格。我们的方法通过三项关键技术进步实现了互动式油画创作。 首先，我们加强了训练阶段的空间对齐和语义增强条件策略，将蒙版和草图映射为空间约束，并从参考图像和文本编码上下文嵌入以形成特征约束，从而实现对象级别的语义对齐。其次，为了克服数据稀缺性的问题，我们提出了一种基于笔触基渲染（SBR）的自我监督风格迁移管道，该方法模拟了油画修复中的填色动态，将真实图像转换为具有保存下来的笔触纹理的风格化油画，以构建大规模成对训练数据集。最后，在推理阶段，我们使用AdaIN操作符整合特征，确保风格一致性。 广泛的实验表明，我们的互动系统能够在保留油画艺术品质的同时进行精细编辑，实现了在风格化油画生成和编辑方面前所未有的想象力实现水平。
URL
https://arxiv.org/abs/2512.08534
