Problem
Research questions and friction points this paper is trying to address.
3D spatial relationships
complex scenes
object movement
image editing
spatial structures
SpatialDiff: 3D-Aware Object Movement via Implicit Spatial Modeling
🤖 AI Summary
本文提出SpatialDiff方法，通过隐式3D空间建模和全局空间监督解决复杂场景中物体移动不准确的问题。
📝 Abstract
Recent advances in image editing allow impressive manipulation of objects, existing methods still struggle to handle spatial movement in complex scenes, such as objects span different depth layers or are partially occluded. Most image editing methods focus solely on prior information from 2D datasets, emphasizing planar features while lacking support for spatial structures. Even approaches that incorporate explicit positional information fail to capture true 3D spatial relationships, thus limiting accurate object movement in complex scenes. In this paper, we present SpatialDiff, a method that effectively captures 3D spatial structures, enabling precise and consistent object movements in complex scenes. Our core innovations are twofold: (1) Implicit 3D Spatial Modeling, which introduces 3D prior knowledge and enables the model to internally build a comprehensive understanding of the three-dimensional spatial structure; and (2) Global Spatial Supervision, which constrains the latent spatial features to enable the model to perceive changes in object spatial positions caused by editing operations. Experimental results demonstrate that our method significantly improves the accuracy and fidelity of spatial movement in complex scenes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Implicit 3D Spatial Modeling
Global Spatial Supervision
Complex Scenes
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