Abstract
We tackle the task of geometric image editing, where an object within an image is repositioned, reoriented, or reshaped while preserving overall scene coherence. Previous diffusion-based editing methods often attempt to handle all relevant subtasks in a single step, proving difficult when transformations become large or structurally complex. We address this by proposing a decoupled pipeline that separates object transformation, source region inpainting, and target region refinement. Both inpainting and refinement are implemented using a training-free diffusion approach, FreeFine. In experiments on our new GeoBench benchmark, which contains both 2D and 3D editing scenarios, FreeFine outperforms state-of-the-art alternatives in image fidelity, and edit precision, especially under demanding transformations. Code and benchmark are available at: this https URL
Abstract (translated)
我们解决了几何图像编辑任务，其中目标是在保持整体场景连贯性的同时，重新定位、旋转或改变图像中某个对象的位置和形状。以往基于扩散的编辑方法通常试图一次性处理所有相关子任务，在进行较大或结构复杂的转换时难度会增加。为了解决这个问题，我们提出了一种解耦管道，将对象变换、源区域修复（inpainting）以及目标区域优化分离出来处理。修复和优化过程均采用无需训练的扩散方法FreeFine实现。在我们的新GeoBench基准测试中进行了实验，该测试包含2D和3D编辑场景，在此基准下，FreeFine在图像保真度和编辑精度上优于现有的最先进替代方案，尤其是在进行具有挑战性的转换时效果更加突出。代码及基准可以在以下网址获取：this https URL
URL
https://arxiv.org/abs/2507.23300
