VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D
  Space
Authors: name: Lin Li, name: Zehuan Huang, name: Haoran Feng, name: Gengxiong Zhuang, name: Rui Chen, name: Chunchao Guo, name: Lu Sheng

3D local editing of specified regions is crucial for game industry and robot
interaction. Recent methods typically edit rendered multi-view images and then
reconstruct 3D models, but they face challenges in precisely preserving
unedited regions and overall coherence. Inspired by structured 3D generative
models, we propose VoxHammer, a novel training-free approach that performs
precise and coherent editing in 3D latent space. Given a 3D model, VoxHammer
first predicts its inversion trajectory and obtains its inverted latents and
key-value tokens at each timestep. Subsequently, in the denoising and editing
phase, we replace the denoising features of preserved regions with the
corresponding inverted latents and cached key-value tokens. By retaining these
contextual features, this approach ensures consistent reconstruction of
preserved areas and coherent integration of edited parts. To evaluate the
consistency of preserved regions, we constructed Edit3D-Bench, a
human-annotated dataset comprising hundreds of samples, each with carefully
labeled 3D editing regions. Experiments demonstrate that VoxHammer
significantly outperforms existing methods in terms of both 3D consistency of
preserved regions and overall quality. Our method holds promise for
synthesizing high-quality edited paired data, thereby laying the data
foundation for in-context 3D generation. See our project page at
https://huanngzh.github.io/VoxHammer-Page/.