Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
Recent advances in sparse voxel representations have significantly improved
the quality of 3D content generation, enabling high-resolution modeling with
fine-grained geometry. However, existing frameworks suffer from severe
computational inefficiencies due to the quadratic complexity of attention
mechanisms in their two-stage diffusion pipelines. In this work, we propose
Ultra3D, an efficient 3D generation framework that significantly accelerates
sparse voxel modeling without compromising quality. Our method leverages the
compact VecSet representation to efficiently generate a coarse object layout in
the first stage, reducing token count and accelerating voxel coordinate
prediction. To refine per-voxel latent features in the second stage, we
introduce Part Attention, a geometry-aware localized attention mechanism that
restricts attention computation within semantically consistent part regions.
This design preserves structural continuity while avoiding unnecessary global
attention, achieving up to 6.7x speed-up in latent generation. To support this
mechanism, we construct a scalable part annotation pipeline that converts raw
meshes into part-labeled sparse voxels. Extensive experiments demonstrate that
Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves
state-of-the-art performance in both visual fidelity and user preference.
6881b243df7c5aafaf37f0c4
Yiwen Chen
claimed_verified
2025-07-31T21:01:15.248Z
64e45b1c2fbf570ac70dc6e8
/avatars/8dd65f75f2c1b92abf2f189f6d8466a2.svg
Chen Yiwen
user
Yiwen-ntu
6881b243df7c5aafaf37f0c5
Zhihao Li
6881b243df7c5aafaf37f0c6
Yikai Wang
6881b243df7c5aafaf37f0c7
Hu Zhang
6881b243df7c5aafaf37f0c8
Qin Li
6881b243df7c5aafaf37f0c9
Chi Zhang
6881b243df7c5aafaf37f0ca
Guosheng Lin
Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
Recent advances in sparse voxel representations have significantly improved
the quality of 3D content generation, enabling high-resolution modeling with
fine-grained geometry. However, existing frameworks suffer from severe
computational inefficiencies due to the quadratic complexity of attention
mechanisms in their two-stage diffusion pipelines. In this work, we propose
Ultra3D, an efficient 3D generation framework that significantly accelerates
sparse voxel modeling without compromising quality. Our method leverages the
compact VecSet representation to efficiently generate a coarse object layout in
the first stage, reducing token count and accelerating voxel coordinate
prediction. To refine per-voxel latent features in the second stage, we
introduce Part Attention, a geometry-aware localized attention mechanism that
restricts attention computation within semantically consistent part regions.
This design preserves structural continuity while avoiding unnecessary global
attention, achieving up to 6.7x speed-up in latent generation. To support this
mechanism, we construct a scalable part annotation pipeline that converts raw
meshes into part-labeled sparse voxels. Extensive experiments demonstrate that
Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves
state-of-the-art performance in both visual fidelity and user preference.
Ultra3D is an efficient 3D generation framework that uses VecSet for coarse layout and Part Attention for refined voxel features, achieving high-resolution generation with improved speed and quality.
sparse voxel representations diffusion pipelines VecSet Part Attention geometry-aware localized attention part-labeled sparse voxels