title: ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid
    Motions
summary: Editing images with instructions to reflect non-rigid motions, camera
  viewpoint shifts, object deformations, human articulations, and complex
  interactions, poses a challenging yet underexplored problem in computer vision.
  Existing approaches and datasets predominantly focus on static scenes or rigid
  transformations, limiting their capacity to handle expressive edits involving
  dynamic motion. To address this gap, we introduce ByteMorph, a comprehensive
  framework for instruction-based image editing with an emphasis on non-rigid
  motions. ByteMorph comprises a large-scale dataset, ByteMorph-6M, and a strong
  baseline model built upon the Diffusion Transformer (DiT), named ByteMorpher.
  ByteMorph-6M includes over 6 million high-resolution image editing pairs for
  training, along with a carefully curated evaluation benchmark ByteMorph-Bench.
  Both capture a wide variety of non-rigid motion types across diverse
  environments, human figures, and object categories. The dataset is constructed
  using motion-guided data generation, layered compositing techniques, and
  automated captioning to ensure diversity, realism, and semantic coherence. We
  further conduct a comprehensive evaluation of recent instruction-based image
  editing methods from both academic and commercial domains.
paper_authors._id: 683ff7f54f32bd7bbca62163
paper_authors.name: Di Chang
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paper_authors.fullname: Di Chang
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paper_authors.user: Boese0601
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paper_authors.name: Mingdeng Cao
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paper_authors.fullname: Mingdeng Cao
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paper_authors.name: Yichun Shi
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paper_authors.name: Bo Liu
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paper_authors.name: Shengqu Cai
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paper_authors.fullname: Shengqu Cai
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paper_authors.user: primecai
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paper_authors.name: Shijie Zhou
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paper_authors.name: Weilin Huang
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paper_authors.name: Gordon Wetzstein
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paper_authors.fullname: Gordon Wetzstein
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paper_authors.name: Mohammad Soleymani
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paper_authors.avatarUrl: /avatars/b85d32f4d7a19816b8d499e05b173ad1.svg
paper_authors.fullname: Mohammad Soleymani
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paper_authors.user: msoleymani
paper_authors._id: 683ff7f54f32bd7bbca6216c
paper_authors.name: Peng Wang
paper_authors.status: extracted_pending
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paper_title: ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid
    Motions
paper_summary: Editing images with instructions to reflect non-rigid motions, camera
  viewpoint shifts, object deformations, human articulations, and complex
  interactions, poses a challenging yet underexplored problem in computer vision.
  Existing approaches and datasets predominantly focus on static scenes or rigid
  transformations, limiting their capacity to handle expressive edits involving
  dynamic motion. To address this gap, we introduce ByteMorph, a comprehensive
  framework for instruction-based image editing with an emphasis on non-rigid
  motions. ByteMorph comprises a large-scale dataset, ByteMorph-6M, and a strong
  baseline model built upon the Diffusion Transformer (DiT), named ByteMorpher.
  ByteMorph-6M includes over 6 million high-resolution image editing pairs for
  training, along with a carefully curated evaluation benchmark ByteMorph-Bench.
  Both capture a wide variety of non-rigid motion types across diverse
  environments, human figures, and object categories. The dataset is constructed
  using motion-guided data generation, layered compositing techniques, and
  automated captioning to ensure diversity, realism, and semantic coherence. We
  further conduct a comprehensive evaluation of recent instruction-based image
  editing methods from both academic and commercial domains.
paper_ai_summary: ByteMorph, a framework using the Diffusion Transformer, addresses non-rigid motion in image editing with a large-scale dataset and comprehensive evaluation.
paper_ai_keywords: Diffusion Transformer ByteMorph ByteMorph-6M ByteMorpher ByteMorph-Bench motion-guided data generation layered compositing automated captioning instruction-based image editing