NANO3D：一种无需掩码的高效三维编辑免训练方法

摘要

三维物体编辑在游戏、动画及机器人领域的交互式内容创作中至关重要，然而现有方法普遍效率低下、一致性不足，且往往难以保持未编辑区域的完整性。多数技术依赖于对多视角渲染图进行编辑后再重建，这一过程易引入伪影，限制了实际应用。为应对这些挑战，我们提出了Nano3D，一个无需训练即可实现精确、连贯三维物体编辑的无掩码框架。Nano3D将FlowEdit融入TRELLIS系统，通过前视图渲染引导局部编辑，并进一步引入了区域感知融合策略——Voxel/Slat-Merge，该策略通过确保编辑与未编辑区域间的一致性，自适应地维护结构保真度。实验表明，Nano3D在三维一致性和视觉质量上均优于现有方法。基于此框架，我们构建了首个大规模三维编辑数据集Nano3D-Edit-100k，包含超过10万对高质量三维编辑样本。本工作不仅解决了算法设计与数据可用性方面的长期难题，显著提升了三维编辑的通用性与可靠性，还为开发前馈式三维编辑模型奠定了坚实基础。项目页面：https://jamesyjl.github.io/Nano3D

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

NANO3D：一种无需掩码的高效三维编辑免训练方法

NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks

摘要

Support