FullPart: Generating each 3D Part at Full Resolution
Authors: name: Lihe Ding, name: Shaocong Dong, name: Yaokun Li, name: Chenjian Gao, name: Xiao Chen, name: Rui Han, name: Yihao Kuang, name: Hong Zhang, name: Bo Huang, name: Zhanpeng Huang, name: Zibin Wang, name: Dan Xu, name: Tianfan Xue

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.