PaintFlow：一种用于交互式油画创作与生成的统一框架
PaintFlow: A Unified Framework for Interactive Oil Paintings Editing and Generation
AI总结：
PaintFlow提出了一种统一框架，通过多模态输入实现交互式油画创作与编辑，利用空间对齐、语义增强和自监督风格迁移技术，提升油画生成与编辑的风格一致性与艺术表现力。
摘要：
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，对数字生成和编辑提出了重大挑战。现有的生成和编辑技术往往受到训练数据分布的限制，并且主要集中在修改真实照片上。在本工作中，我们介绍了一种统一的多模态框架，用于油画生成和编辑。所提出的系统允许用户通过参考图像进行精确的语义控制，通过手绘草图进行空间结构对齐，并通过自然语言提示进行高级语义指导，同时在所有输出中保持一致的绘画风格。我们的方法通过三个关键技术进步实现了交互式油画创作。首先，我们在训练阶段增强了空间对齐和语义增强的条件策略，将掩码和草图映射到空间约束，并将来自参考图像和文本的上下文嵌入编码为特征约束，从而实现对象级的语义对齐。其次，为克服数据稀缺问题，我们提出了一种基于笔触渲染（SBR）的自监督风格迁移管道，该管道模拟了油画修复的修补动态，将真实图像转换为具有保留笔触纹理的风格化油画，以构建大规模配对训练数据集。最后，在推理过程中，我们使用AdaIN操作符整合特征，以确保风格的一致性。广泛的实验表明，我们的交互系统能够在保持油画艺术品质的同时实现细粒度编辑，并在风格化油画生成和编辑中实现了前所未有的想象力实现水平。
英文摘要：
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.
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