Nano3D revolutionizes 3D asset editing by enabling training-free, part-level shape modifications like removal, addition, and replacement without any manual masks. Developed by researchers from Tsinghua University, Peking University, HKUST, CASIA, and ShengShu, Nano3D bridges the gap between text-driven 2D editing and 3D object manipulation. Unlike existing 3D editing methods that require time-consuming optimization or mask annotations, Nano3D combines FlowEdit and TRELLIS to perform precise, localized 3D edits directly on voxel-based representations.
Key Highlights:
- Training-Free, Mask-Free Editing: Achieves high-quality, localized 3D edits (add, remove, replace) using only pretrained models with no finetuning or masks required.
- FlowEdit + TRELLIS Integration: Extends the inversion-free image editing technique FlowEdit into 3D space, ensuring semantic alignment and geometric fidelity.
- Voxel/Slat-Merge Strategy: Introduces a novel region-aware merging process to preserve geometry and texture consistency across unedited regions.
- Nano3D-Edit-100k Dataset: Builds the first large-scale 3D editing dataset with over 100,000 paired samples for future feed-forward 3D editing models.
- State-of-the-Art Results: Outperforms Tailor3D, Vox-E, and TRELLIS with 2× better structure preservation, highest visual quality, and best semantic alignment across multi-view edits.
Why It Matters:
Nano3D marks a leap forward in interactive 3D content creation for gaming, AR/VR, and robotics. By enabling editable 3D assets without retraining or masks, it makes geometry-aware, semantically consistent 3D editing accessible to creators and developers alike. With Nano3D, the dream of real-time, instruction-driven 3D customization from “add a sword” to “remove the chair” becomes reality.
Explore More:
- Paper: arXiv:2510.15019
- Project Page: https://jamesyjl.github.io/Nano3D
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