Coupled Diffusion Sampling for Training-Free Multi-View Image Editing

• URL: http://arxiv.org/abs/2510.14981v1
• Date: Thu, 16 Oct 2025 17:59:59 GMT
• Title: Coupled Diffusion Sampling for Training-Free Multi-View Image Editing
• Authors: Hadi Alzayer, Yunzhi Zhang, Chen Geng, Jia-Bin Huang, Jiajun Wu,
• Abstract summary: Pre-trained 2D image editing models can independently produce high-quality edits for each image in a set of multi-view images, but they do not maintain consistency across views.<n>We propose an implicit 3D regularization approach by constraining the generated 2D image sequences to adhere to a pre-trained multi-view image distribution.<n>This is achieved through coupled diffusion sampling, a simple diffusion sampling technique that concurrently samples two trajectories from both a multi-view image distribution and a 2D edited image distribution.
• Score: 27.884718441352387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an inference-time diffusion sampling method to perform multi-view consistent image editing using pre-trained 2D image editing models. These models can independently produce high-quality edits for each image in a set of multi-view images of a 3D scene or object, but they do not maintain consistency across views. Existing approaches typically address this by optimizing over explicit 3D representations, but they suffer from a lengthy optimization process and instability under sparse view settings. We propose an implicit 3D regularization approach by constraining the generated 2D image sequences to adhere to a pre-trained multi-view image distribution. This is achieved through coupled diffusion sampling, a simple diffusion sampling technique that concurrently samples two trajectories from both a multi-view image distribution and a 2D edited image distribution, using a coupling term to enforce the multi-view consistency among the generated images. We validate the effectiveness and generality of this framework on three distinct multi-view image editing tasks, demonstrating its applicability across various model architectures and highlighting its potential as a general solution for multi-view consistent editing.

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