全分辨率生成：逐部件构建高精度三维模型

FullPart: Generating each 3D Part at Full Resolution

October 30, 2025

作者: Lihe Ding, Shaocong Dong, Yaokun Li, Chenjian Gao, Xiao Chen, Rui Han, Yihao Kuang, Hong Zhang, Bo Huang, Zhanpeng Huang, Zibin Wang, Dan Xu, Tianfan Xue

cs.AI

摘要

基于部件的三维生成技术具有广泛的应用前景。现有部件生成方法中，采用隐式向量集表征的方式常因几何细节不足而受限；另一类采用显式体素表征的方法虽共享全局体素网格，却导致细小部件所占体素过少而质量下降。本文提出创新框架FullPart，融合隐式与显式范式的优势：首先通过隐式边界框向量集扩散过程生成布局（该任务适合隐式扩散处理，因边界框标记仅含少量几何信息），随后在各部件独立的固定全分辨率体素网格中生成细节部件。相较于共享低分辨率空间的方法，本框架即使对微小部件也采用全分辨率生成，从而实现精细细节的合成。针对不同尺寸部件间信息交互的错位问题，我们进一步提出中心点编码策略以保持全局一致性。此外，为缓解可靠部件数据匮乏的现状，我们构建了迄今最大规模的人工标注三维部件数据集PartVerse-XL，包含4万个物体与32万个部件。大量实验表明，FullPart在三维部件生成任务上达到了最先进的性能水平。我们将公开全部代码、数据与模型，以促进三维部件生成领域的后续研究。

English

Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficient geometric details. Another line of work adopts an explicit voxel representation but shares a global voxel grid among all parts; this often causes small parts to occupy too few voxels, leading to degraded quality. In this paper, we propose FullPart, a novel framework that combines both implicit and explicit paradigms. It first derives the bounding box layout through an implicit box vector-set diffusion process, a task that implicit diffusion handles effectively since box tokens contain little geometric detail. Then, it generates detailed parts, each within its own fixed full-resolution voxel grid. Instead of sharing a global low-resolution space, each part in our method - even small ones - is generated at full resolution, enabling the synthesis of intricate details. We further introduce a center-point encoding strategy to address the misalignment issue when exchanging information between parts of different actual sizes, thereby maintaining global coherence. Moreover, to tackle the scarcity of reliable part data, we present PartVerse-XL, the largest human-annotated 3D part dataset to date with 40K objects and 320K parts. Extensive experiments demonstrate that FullPart achieves state-of-the-art results in 3D part generation. We will release all code, data, and model to benefit future research in 3D part generation.