EditP23: 3D Editing via Propagation of Image Prompts to Multi-View

• URL: http://arxiv.org/abs/2506.20652v1
• Date: Wed, 25 Jun 2025 17:50:20 GMT
• Title: EditP23: 3D Editing via Propagation of Image Prompts to Multi-View
• Authors: Roi Bar-On, Dana Cohen-Bar, Daniel Cohen-Or,
• Abstract summary: We present EditP23, a method for mask-free 3D editing that propagates 2D image edits to multi-view representations in a 3D-consistent manner.<n>In contrast to traditional approaches that rely on text-based prompting or explicit spatial masks, EditP23 enables intuitive edits by conditioning on a pair of images.
• Score: 49.5776584729283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present EditP23, a method for mask-free 3D editing that propagates 2D image edits to multi-view representations in a 3D-consistent manner. In contrast to traditional approaches that rely on text-based prompting or explicit spatial masks, EditP23 enables intuitive edits by conditioning on a pair of images: an original view and its user-edited counterpart. These image prompts are used to guide an edit-aware flow in the latent space of a pre-trained multi-view diffusion model, allowing the edit to be coherently propagated across views. Our method operates in a feed-forward manner, without optimization, and preserves the identity of the original object, in both structure and appearance. We demonstrate its effectiveness across a range of object categories and editing scenarios, achieving high fidelity to the source while requiring no manual masks.

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