UltraShape 1.0：透過可擴展幾何細化實現高保真度三維形狀生成

UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement

December 24, 2025

作者: Tanghui Jia, Dongyu Yan, Dehao Hao, Yang Li, Kaiyi Zhang, Xianyi He, Lanjiong Li, Jinnan Chen, Lutao Jiang, Qishen Yin, Long Quan, Ying-Cong Chen, Li Yuan

cs.AI

摘要

本報告介紹 UltraShape 1.0——一個可擴展的三維擴散框架，專注於生成高保真度的三維幾何形體。該方法採用兩階段生成流程：首先生成粗略的全局結構，再進行細化以產出細節豐富的高品質幾何體。為實現可靠的三維生成，我們開發了完整的數據處理流程，包含創新的水密性處理方法與高品質數據過濾機制。此流程通過剔除低品質樣本、填充孔洞及加厚薄壁結構，在保留細粒度幾何細節的同時，顯著提升了公開三維數據集的幾何品質。為實現細粒度幾何優化，我們在擴散過程中將空間定位與幾何細節合成解耦：通過在固定空間位置執行體素級精煉，以粗粒度幾何體導出的體素查詢作為透過RoPE編碼的顯式位置錨點，使擴散模型能專注於在縮減的結構化解空間內合成局部幾何細節。我們的模型僅使用公開三維數據集進行訓練，在有限訓練資源下仍實現了卓越的幾何品質。大量評估表明，UltraShape 1.0 在數據處理品質與幾何生成能力上均與現有開源方法具備競爭力。所有程式碼與訓練模型將公開釋出以支援後續研究。

English

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.