Existing 3D editing methods rely on computationally intensive scene-by-scene iterative optimization and suffer from multi-view inconsistency. We propose an effective and feedforward 3D editing framework based on the TRELLIS generative backbone, capable of modifying 3D models from a single editing view. Our framework addresses two key issues: adapting training-free 2D editing to structured 3D representations, and overcoming the bottleneck of appearance fidelity in compressed 3D features. To ensure geometric consistency, we introduce Voxel FlowEdit , an edit-driven flow in the sparse voxel latent space that achieves globally consistent 3D deformation in a single pass. To restore high-fidelity details, we develop a normal-guided single to multi-view generation module as an external appearance prior, successfully recovering high-frequency textures. Experiments demonstrate that our method enables fast, globally consistent, and high-fidelity 3D model editing.
Methodology
The framework operates in two main stages: Geometry Editing and Texture Refinement.
Starting from a rendered source view, an edited target image provides the guidance for editing.
In the Geometry Editing stage, the Voxel FlowEdit algorithm transforms the source voxel structure under flow-based guidance, followed by SLAT Repainting that refines local latent features to produce the target mesh.
The Texture Refinement stage then employs a generation branch and a normal-guided control adapter to synthesize multi-view-consistent textures, which are projected and fused onto the mesh to yield the final high-fidelity 3D asset.
Editing Results
Click on the cards to view extracted GLB files.
Comparison
We compare our Easy3E with other 3D editing methods.
Citation
If you find our work useful, please consider citing:
@inproceedings{hu2026easy3e,
title={Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flow},
author={Hu, Shimin and Wei, Yuanyi and Zha, Fei and Guo, Yudong and Zhang, Juyong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2026}
}
