UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipeline: a coarse global structure is first synthesized and then refined to produce detailed, high-quality geometry. To support reliable 3D generation, we develop a comprehensive data processing pipeline that includes a novel watertight processing method and high-quality data filtering. This pipeline improves the geometric quality of publicly available 3D datasets by removing low-quality samples, filling holes, and thickening thin structures, while preserving fine-grained geometric details. To enable fine-grained geometry refinement, we decouple spatial localization from geometric detail synthesis in the diffusion process. We achieve this by performing voxel-based refinement at fixed spatial locations, where voxel queries derived from coarse geometry provide explicit positional anchors encoded via RoPE, allowing the diffusion model to focus on synthesizing local geometric details within a reduced, structured solution space. Our model is trained exclusively on publicly available 3D datasets, achieving strong geometric quality despite limited training resources. Extensive evaluations demonstrate that UltraShape 1.0 performs competitively with existing open-source methods in both data processing quality and geometry generation. All code and trained models will be released to support future research.
694e459a746a34b55dd54592
Tanghui Jia
claimed_verified
2025-12-31T20:57:47.389Z
62fbe6cfa80632fbd47bb8ca
https://cdn-avatars.huggingface.co/v1/production/uploads/62fbe6cfa80632fbd47bb8ca/lIOS1wWIiBkfjfY3cNJRb.jpeg
thj2333
infinith
user
infinith
694e459a746a34b55dd54593
Dongyu Yan
claimed_verified
2025-12-31T20:57:43.637Z
64049ae20ab5e22719f35103
https://cdn-avatars.huggingface.co/v1/production/uploads/1678023295407-noauth.jpeg
Dongyu Yan
StarYDY
user
StarYDY
694e459a746a34b55dd54594
Dehao Hao
694e459a746a34b55dd54595
Yang Li
admin_assigned
2026-01-02T15:24:51.861Z
641d626b3f04f9bf2da1b2a2
https://cdn-avatars.huggingface.co/v1/production/uploads/641d626b3f04f9bf2da1b2a2/0Mp8D-rytKDngM8jEMfUn.jpeg
Yang Li
YukinoshitaYukino
user
YukinoshitaYukino
694e459a746a34b55dd54596
Kaiyi Zhang
694e459a746a34b55dd54597
Xianyi He
694e459a746a34b55dd54598
Lanjiong Li
694e459a746a34b55dd54599
Jinnan Chen
694e459a746a34b55dd5459a
Lutao Jiang
claimed_verified
2025-12-31T20:57:40.803Z
65d61d8fd484956b5acc89fe
/avatars/47954232c90780ffe898a5a445f7fb0a.svg
Lutao Jiang
LutaoJiang
user
LutaoJiang
694e459a746a34b55dd5459b
Qishen Yin
694e459a746a34b55dd5459c
Long Quan
694e459a746a34b55dd5459d
Ying-Cong Chen
694e459a746a34b55dd5459e
Li Yuan
UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipeline: a coarse global structure is first synthesized and then refined to produce detailed, high-quality geometry. To support reliable 3D generation, we develop a comprehensive data processing pipeline that includes a novel watertight processing method and high-quality data filtering. This pipeline improves the geometric quality of publicly available 3D datasets by removing low-quality samples, filling holes, and thickening thin structures, while preserving fine-grained geometric details. To enable fine-grained geometry refinement, we decouple spatial localization from geometric detail synthesis in the diffusion process. We achieve this by performing voxel-based refinement at fixed spatial locations, where voxel queries derived from coarse geometry provide explicit positional anchors encoded via RoPE, allowing the diffusion model to focus on synthesizing local geometric details within a reduced, structured solution space. Our model is trained exclusively on publicly available 3D datasets, achieving strong geometric quality despite limited training resources. Extensive evaluations demonstrate that UltraShape 1.0 performs competitively with existing open-source methods in both data processing quality and geometry generation. All code and trained models will be released to support future research.