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The success of image generative models has enabled us to build methods that can edit images based on text or other user input. However, these methods are bespoke, imprecise, require additional information, or are limited to only 2D image edits. We present GeoDiffuser, a zero-shot optimization-based method that unifies common 2D and 3D image-based object editing capabilities into a single method. Our key insight is to view image editing operations as geometric transformations. We show that these transformations can be directly incorporated into the attention layers in diffusion models to implicitly perform editing operations. Our training-free optimization method uses an objective function that seeks to preserve object style but generate plausible images, for instance with accurate lighting and shadows. It also inpaints disoccluded parts of the image where the object was originally located. Given a natural image and user input, we segment the foreground object using SAM and estimate a corresponding transform which is used by our optimization approach for editing. GeoDiffuser can perform common 2D and 3D edits like object translation, 3D rotation, and removal. We present quantitative results, including a perceptual study, that shows how our approach is better than existing methods.
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This content will become publicly available on November 8, 2025
HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness
This paper addresses the challenge of precisely swapping objects in videos, particularly those involved in hand-object interactions (HOI), using a single user-provided reference object image. While diffusion models have advanced video editing, they struggle with the complexities of HOI, often failing to generate realistic edits when object swaps involve changes in shape or functionality. To overcome this, the authors propose HOI-Swap, a novel diffusion-based video editing framework trained in a self-supervised manner. The framework operates in two stages: (1) single-frame object swapping with HOI awareness, where the model learns to adjust interaction patterns (e.g., hand grasp) based on object property changes; and (2) sequence-wide extension, where motion alignment is achieved by warping a sequence from the edited frame using sampled motion points and conditioning generation on the warped sequence. Extensive qualitative and quantitative evaluations demonstrate that HOI-Swap significantly outperforms prior methods, producing high-quality, realistic HOI video edits.
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- Award ID(s):
- 2505865
- PAR ID:
- 10631939
- Publisher / Repository:
- https://doi.org/10.48550/arXiv.2406.07754
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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