解决遮挡物体移动难题，让图像编辑更自然真实。
A Diffusion-Based Framework for Occluded Object Movement
- 双分支并行处理：一边补全遮挡部分，一边规划移动路径。
- 补全区域精准聚焦，移动后与新环境融合自然。
- 适合需要精确编辑遮挡物体的设计师和研究人员。
在图像编辑中无缝移动物体是常见需求，但现有方法对真实图像中的遮挡情况仍难以应对。主要难点在于需先完成被遮挡部分才能进行移动。为此，我们提出基于扩散模型的专用框架DiffOOM，通过两个并行分支同时实现物体去遮挡与移动。去遮挡分支采用背景色填充策略和持续更新的物体掩码，将扩散过程聚焦于补全目标物体的被遮挡区域。移动分支则利用潜在空间优化将完整物体放置到目标位置，并结合局部文本条件引导，使其自然融入新环境。大量实验验证了方法的优越性，用户研究进一步证实其有效性。
原文摘要 · Abstract (English)
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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