ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid
  Motions
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.
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Di Chang
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Di Chang
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Mingdeng Cao
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Mingdeng Cao
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Yichun Shi
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Bo Liu
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Shengqu Cai
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Shengqu Cai
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Shijie Zhou
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Shijie Zhou
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Weilin Huang
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Gordon Wetzstein
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Gordon Wetzstein
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wetzste1
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Mohammad Soleymani
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2025-06-05T07:35:42.327Z
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Mohammad Soleymani
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msoleymani
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Peng Wang
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2025-06-04T07:38:32.749Z
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peng  wang
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Peng-Wang
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid
  Motions
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.
ByteMorph, a framework using the Diffusion Transformer, addresses non-rigid motion in image editing with a large-scale dataset and comprehensive evaluation.
Diffusion Transformer ByteMorph ByteMorph-6M ByteMorpher ByteMorph-Bench motion-guided data generation layered compositing automated captioning instruction-based image editing