VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D
  Space
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/.
68ae6936364411bea07df706
Lin Li
claimed_verified
2025-08-27T07:10:57.843Z
677c938c7d036e49073e2989
/avatars/6f308ddbbd8c8eba8ecffbc28f4c941b.svg
Li Lin
user
Nelipot
68ae6936364411bea07df707
Zehuan Huang
claimed_verified
2025-08-27T12:45:44.862Z
6375d136dee28348a9c63cbf
https://cdn-avatars.huggingface.co/v1/production/uploads/6375d136dee28348a9c63cbf/gK465HBrQWIOZ-qtHb-Vh.jpeg
zehuan-huang
user
huanngzh
68ae6936364411bea07df708
Haoran Feng
claimed_verified
2025-08-27T07:11:01.785Z
65240d0ca801972b6eb12ed8
https://cdn-avatars.huggingface.co/v1/production/uploads/65240d0ca801972b6eb12ed8/hl2RAssBperb5JlgOIDvw.jpeg
Haoran Feng
user
fenghora
68ae6936364411bea07df709
Gengxiong Zhuang
68ae6936364411bea07df70a
Rui Chen
68ae6936364411bea07df70b
Chunchao Guo
68ae6936364411bea07df70c
Lu Sheng
claimed_verified
2025-08-27T14:59:47.256Z
65b722dbe02a17f0f8d1cc6b
/avatars/65f20601ef9b8ebfdddadd737f9153d6.svg
Lu Sheng
user
lsheng2024
VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D
  Space
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/.
VoxHammer is a training-free method that performs precise and coherent 3D editing in latent space, ensuring consistency in preserved regions and high-quality overall results.
structured 3D generative models VoxHammer 3D latent space inversion trajectory inverted latents key-value tokens denoising Edit3D-Bench 3D consistency in-context 3D generation