title: PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
summary: 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.
paper_authors._id: 67bbed806f2833ecccf914dd
paper_authors.name: Shijie Huang
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paper_authors.fullname: Shijie Huang
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paper_authors.name: Yiren Song
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paper_authors.fullname: Yiren Song
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paper_authors.name: Yuxuan Zhang
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paper_authors.fullname: yuxuan
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paper_authors.name: Hailong Guo
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paper_authors.name: Xueyin Wang
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paper_authors._id: 65fd9853b329ebf2d40e280a
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paper_authors.fullname: Wang Xueying
paper_authors.type: user
paper_authors.user: Forever-rover
paper_authors._id: 67bbed806f2833ecccf914e2
paper_authors.name: Mike Zheng Shou
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paper_authors.fullname: Mike Zheng Shou
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paper_authors.user: AnalMom
paper_authors._id: 67bbed806f2833ecccf914e3
paper_authors.name: Jiaming Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:40:39.635Z
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paper_authors.fullname: LIU JIAMING
paper_authors.type: user
paper_authors.user: jamesliu1217
paper_title: PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
paper_summary: 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.
paper_ai_summary: PhotoDoodle is an image editing framework that uses a two-stage训练 strategy and EditLoRA for realistic blending and perspective alignment, enabling the overlay of decorative elements on photographs with preserved background and artist-specific styles.
paper_ai_keywords: PhotoDoodle OmniEditor EditLoRA positional encoding reuse mechanism