PhotoDoodle：从少样本成对数据学习艺术图像编辑
PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
AI总结：
PhotoDoodle提出两阶段训练框架，结合OmniEditor与EditLoRA，从少样本成对数据学习艺术图像编辑，实现装饰元素无缝融合与风格保留。
摘要：
我们介绍了PhotoDoodle，一种新颖的图像编辑框架，旨在通过使艺术家能够将装饰元素叠加到照片上来促进照片涂鸦。照片涂鸦具有挑战性，因为插入的元素必须与背景无缝集成，需要逼真的混合、透视对齐和上下文一致性。此外，背景必须保持不变形，并且必须从有限的训练数据中高效捕获艺术家的独特风格。这些要求是先前主要关注全局风格迁移或区域修复的方法所未能解决的。所提出的方法PhotoDoodle采用两阶段训练策略。最初，我们使用大规模数据训练一个通用图像编辑模型OmniEditor。随后，我们使用EditLoRA在一个由艺术家精选的前后图像对的小型数据集上微调该模型，以捕获不同的编辑风格和技术。为了增强生成结果的一致性，我们引入了一种位置编码重用机制。此外，我们发布了一个包含六种高质量风格的PhotoDoodle数据集。大量实验证明了我们方法在定制图像编辑中的先进性能和鲁棒性，为艺术创作开辟了新的可能性。
英文摘要：
We introduce PhotoDoodle, a novel image editing framework designed to facilitate photo doodling by enabling artists to overlay decorative elements onto photographs. Photo doodling is challenging because the inserted elements must appear seamlessly integrated with the background, requiring realistic blending, perspective alignment, and contextual coherence. Additionally, the background must be preserved without distortion, and the artist's unique style must be captured efficiently from limited training data. These requirements are not addressed by previous methods that primarily focus on global style transfer or regional inpainting. The proposed method, PhotoDoodle, employs a two-stage training strategy. Initially, we train a general-purpose image editing model, OmniEditor, using large-scale data. Subsequently, we fine-tune this model with EditLoRA using a small, artist-curated dataset of before-and-after image pairs to capture distinct editing styles and techniques. To enhance consistency in the generated results, we introduce a positional encoding reuse mechanism. Additionally, we release a PhotoDoodle dataset featuring six high-quality styles. Extensive experiments demonstrate the advanced performance and robustness of our method in customized image editing, opening new possibilities for artistic creation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
