无需训练和掩码，实现高效精准的3D物体局部编辑。
NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks
- 基于前视图引导，用FlowEdit在TRELLIS中实现局部编辑
- 引入体素/条带自适应融合策略，保持编辑与未编辑区域一致
- 构建首个超10万对的高质量3D编辑数据集，推动模型发展
3D物体编辑在游戏、动画和机器人领域至关重要，但现有方法效率低、不一致且难以保留未编辑区域。多数方法依赖多视角渲染后重建，导致伪影并限制实用性。为此，我们提出Nano3D，一种无需训练且无需掩码的精确、连贯3D编辑框架。Nano3D将FlowEdit集成至TRELLIS，通过前视图引导进行局部编辑，并引入区域感知融合策略——体素/条带融合（Voxel/Slat-Merge），自适应保证编辑区与未编辑区的一致性。实验表明，Nano3D在3D一致性与视觉质量上优于现有方法。基于此框架，我们构建了首个大规模3D编辑数据集Nano3D-Edit-100k，包含超过10万对高质量3D编辑样本。该工作解决了算法设计与数据可用性的长期挑战，显著提升3D编辑的通用性与可靠性，为前馈式3D编辑模型的发展奠定基础。
原文摘要 · Abstract (English)
3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to preserve unedited regions. Most methods rely on editing multi-view renderings followed by reconstruction, which introduces artifacts and limits practicality. To address these challenges, we propose Nano3D, a training-free framework for precise and coherent 3D object editing without masks. Nano3D integrates FlowEdit into TRELLIS to perform localized edits guided by front-view renderings, and further introduces region-aware merging strategies, Voxel/Slat-Merge, which adaptively preserve structural fidelity by ensuring consistency between edited and unedited areas. Experiments demonstrate that Nano3D achieves superior 3D consistency and visual quality compared with existing methods. Based on this framework, we construct the first large-scale 3D editing datasets Nano3D-Edit-100k, which contains over 100,000 high-quality 3D editing pairs. This work addresses long-standing challenges in both algorithm design and data availability, significantly improving the generality and reliability of 3D editing, and laying the groundwork for the development of feed-forward 3D editing models. Project Page:https://jamesyjl.github.io/Nano3D
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