基于扩散的遮挡物体移动框架
A Diffusion-Based Framework for Occluded Object Movement
AI总结：
针对图像编辑中遮挡物体移动的难题，提出基于扩散的框架DiffOOM，通过去遮挡和移动两个并行分支同时完成物体补全与位置转移，实现优越性能。
摘要：
在场景中无缝移动物体是图像编辑的常见需求，但对现有编辑方法而言仍是一项挑战。尤其是对于真实世界图像，遮挡情况进一步增加了难度。主要难点在于，被遮挡的部分需要先被补全，然后才能进行移动。为了利用预训练扩散模型中嵌入的真实世界知识，我们提出了一个专门为遮挡物体移动设计的基于扩散的框架，命名为 DiffOOM。所提出的 DiffOOM 由两个并行分支组成，同时执行物体去遮挡和移动。去遮挡分支利用背景颜色填充策略和持续更新的物体掩码，将扩散过程集中于补全目标物体被遮挡的部分。同时，移动分支采用潜在优化将补全后的物体放置在目标位置，并采用局部文本条件引导，将物体适当地融入新环境。广泛的评估证明了我们方法的优越性能，该性能进一步通过一项全面的用户研究得到了验证。
英文摘要：
Seamlessly moving objects within a scene is a common requirement for image editing, but it is still a challenge for existing editing methods. Especially for real-world images, the occlusion situation further increases the difficulty. The main difficulty is that the occluded portion needs to be completed before movement can proceed. To leverage the real-world knowledge embedded in the pre-trained diffusion models, we propose a Diffusion-based framework specifically designed for Occluded Object Movement, named DiffOOM. The proposed DiffOOM consists of two parallel branches that perform object de-occlusion and movement simultaneously. The de-occlusion branch utilizes a background color-fill strategy and a continuously updated object mask to focus the diffusion process on completing the obscured portion of the target object. Concurrently, the movement branch employs latent optimization to place the completed object in the target location and adopts local text-conditioned guidance to integrate the object into new surroundings appropriately. Extensive evaluations demonstrate the superior performance of our method, which is further validated by a comprehensive user study.
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