标题： ByteMorph：基准测试带有非刚性运动的指令引导图像编辑 显示英文标题

标题： ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions

摘要： 使用指令来编辑图像以反映非刚性运动、相机视角变化、物体变形、人体关节活动以及复杂交互，这一问题在计算机视觉领域具有挑战性且尚未被充分研究。 现有的方法和数据集大多侧重于静态场景或刚性变换，限制了它们处理涉及动态运动的表达性编辑的能力。 为了解决这一问题，我们提出了 ByteMorph，这是一种基于指令的图像编辑综合框架，重点在于非刚性运动。 ByteMorph 包含一个大规模数据集 ByteMorph-6M 和一个强大的基准模型 ByteMorpher，该模型基于扩散Transformer（DiT）构建。 ByteMorph-6M 包含超过600万对高分辨率图像编辑样本用于训练，并附带了一个精心设计的评估基准 ByteMorph-Bench。 两者都涵盖了多样化环境中各种类型的非刚性运动、人体形态以及物体类别。 数据集通过运动引导的数据生成、分层合成技术以及自动化标题生成构建，确保多样性、真实性和语义一致性。 此外，我们还对来自学术界和商业领域的近期基于指令的图像编辑方法进行了全面评估。 显示英文摘要

摘要： 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.