Towards Scalable and Consistent 3D Editing
3D editing - the task of locally modifying the geometry or appearance of a 3D
asset - has wide applications in immersive content creation, digital
entertainment, and AR/VR. However, unlike 2D editing, it remains challenging
due to the need for cross-view consistency, structural fidelity, and
fine-grained controllability. Existing approaches are often slow, prone to
geometric distortions, or dependent on manual and accurate 3D masks that are
error-prone and impractical. To address these challenges, we advance both the
data and model fronts. On the data side, we introduce 3DEditVerse, the largest
paired 3D editing benchmark to date, comprising 116,309 high-quality training
pairs and 1,500 curated test pairs. Built through complementary pipelines of
pose-driven geometric edits and foundation model-guided appearance edits,
3DEditVerse ensures edit locality, multi-view consistency, and semantic
alignment. On the model side, we propose 3DEditFormer, a
3D-structure-preserving conditional transformer. By enhancing image-to-3D
generation with dual-guidance attention and time-adaptive gating, 3DEditFormer
disentangles editable regions from preserved structure, enabling precise and
consistent edits without requiring auxiliary 3D masks. Extensive experiments
demonstrate that our framework outperforms state-of-the-art baselines both
quantitatively and qualitatively, establishing a new standard for practical and
scalable 3D editing. Dataset and code will be released. Project:
https://www.lv-lab.org/3DEditFormer/
68e8875695e8e6771df38ac1
Ruihao Xia
claimed_verified
2025-10-10T04:31:48.948Z
65896f5bae21a8ff2883ec1a
/avatars/ffacc166fd0d145504773c666b7c8b94.svg
Ruihao Xia
user
XiaRho
68e8875695e8e6771df38ac2
Yang Tang
68e8875695e8e6771df38ac3
Pan Zhou
Towards Scalable and Consistent 3D Editing
3D editing - the task of locally modifying the geometry or appearance of a 3D
asset - has wide applications in immersive content creation, digital
entertainment, and AR/VR. However, unlike 2D editing, it remains challenging
due to the need for cross-view consistency, structural fidelity, and
fine-grained controllability. Existing approaches are often slow, prone to
geometric distortions, or dependent on manual and accurate 3D masks that are
error-prone and impractical. To address these challenges, we advance both the
data and model fronts. On the data side, we introduce 3DEditVerse, the largest
paired 3D editing benchmark to date, comprising 116,309 high-quality training
pairs and 1,500 curated test pairs. Built through complementary pipelines of
pose-driven geometric edits and foundation model-guided appearance edits,
3DEditVerse ensures edit locality, multi-view consistency, and semantic
alignment. On the model side, we propose 3DEditFormer, a
3D-structure-preserving conditional transformer. By enhancing image-to-3D
generation with dual-guidance attention and time-adaptive gating, 3DEditFormer
disentangles editable regions from preserved structure, enabling precise and
consistent edits without requiring auxiliary 3D masks. Extensive experiments
demonstrate that our framework outperforms state-of-the-art baselines both
quantitatively and qualitatively, establishing a new standard for practical and
scalable 3D editing. Dataset and code will be released. Project:
https://www.lv-lab.org/3DEditFormer/
A new framework, 3DEditFormer, uses a 3D-structure-preserving conditional transformer to enable precise and consistent 3D editing without manual masks, outperforming existing methods.
3D editing 3DEditVerse 3DEditFormer 3D-structure-preserving conditional transformer dual-guidance attention time-adaptive gating edit locality multi-view consistency semantic alignment image-to-3D generation