NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks
Junliang Ye, Shenghao Xie, Ruowen Zhao, Zhengyi Wang, Hongyu Yan, Wenqiang Zu, Lei Ma, Jun Zhu
2025-10-20
Summary
This paper introduces Nano3D, a new way to edit 3D objects directly without needing complicated setups or pre-existing masks. It focuses on making the editing process more accurate, consistent, and easier to use for things like video games, animation, and robotics.
What's the problem?
Currently, editing 3D objects is difficult because most methods involve changing images of the object from multiple angles and then trying to rebuild the 3D model. This process often creates visual errors and isn't very practical. Existing techniques also struggle to edit only specific parts of an object without messing up the rest of it, and they often lack consistency across different views.
What's the solution?
Nano3D solves this by using a technique called FlowEdit within a system called TRELLIS. This allows for localized edits based on how the object looks from the front. Crucially, it also introduces new ways to blend the edited and unedited parts, called Voxel/Slat-Merge, which intelligently preserve the original shape and structure of the object while allowing for precise changes. The researchers also created a large dataset of 3D editing examples to help train and test future editing tools.
Why it matters?
This work is important because it significantly improves the quality and reliability of 3D object editing. By making the process more direct and consistent, it opens the door for easier creation of 3D content and the development of more advanced, automated 3D editing tools. The new dataset also provides a valuable resource for researchers in this field, accelerating progress in the area.
Abstract
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