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.
@article{arxiv.2506.20652,
title = {EditP23: 3D Editing via Propagation of Image Prompts to Multi-View},
author = {Roi Bar-On and Dana Cohen-Bar and Daniel Cohen-Or},
journal= {arXiv preprint arXiv:2506.20652},
year = {2025}
}
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Code, supplementary videos, interactive 3D visualizations, and additional results are available at https://editp23.github.io/