NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks
Authors: name: Junliang Ye, name: Shenghao Xie, name: Ruowen Zhao, name: Zhengyi Wang, name: Hongyu Yan, name: Wenqiang Zu, name: Lei Ma, name: Jun Zhu

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