无需训练，一键修复3D模型编辑后的几何失真问题。
Make Your MoVe: Make Your 3D Contents by Adapting Multi-View Diffusion Models to External Editing
- 用原始法向量潜变量引导生成，保持几何结构不变
- 单次推理实现多视图一致性与网格质量显著提升
- 适配多种3D生成与编辑工具，即插即用
随着3D生成技术的发展，用户对个性化内容的需求日益增长。人们希望对生成的3D内容进行风格、颜色和光照等编辑，同时保持其原始几何结构。然而，现有编辑工具多针对2D图像设计，直接将结果输入多视图扩散模型会导致信息损失，降低3D资产质量。本文提出一种无需微调、即插即用的方法，在单次推理中实现编辑内容与原始几何的一致性。核心是几何保持模块，利用原始输入的法向量潜变量指导生成；同时引入注入开关机制，灵活控制原始法向量的监督强度，确保颜色与法向视图间的对齐。大量实验表明，该方法在多种多视图扩散模型与编辑方法组合下，均能显著提升多视图一致性和网格质量。
原文摘要 · Abstract (English)
As 3D generation techniques continue to flourish, the demand for generating personalized content is rapidly rising. Users increasingly seek to apply various editing methods to polish generated 3D content, aiming to enhance its color, style, and lighting without compromising the underlying geometry. However, most existing editing tools focus on the 2D domain, and directly feeding their results into 3D generation methods (like multi-view diffusion models) will introduce information loss, degrading the quality of the final 3D assets. In this paper, we propose a tuning-free, plug-and-play scheme that aligns edited assets with their original geometry in a single inference run. Central to our approach is a geometry preservation module that guides the edited multi-view generation with original input normal latents. Besides, an injection switcher is proposed to deliberately control the supervision extent of the original normals, ensuring the alignment between the edited color and normal views. Extensive experiments show that our method consistently improves both the multi-view consistency and mesh quality of edited 3D assets, across multiple combinations of multi-view diffusion models and editing methods.
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