HY3D-Bench: Generation of 3D Assets
Authors: name: Team Hunyuan3D, name: Bowen Zhang, name: Chunchao Guo, name: Dongyuan Guo, name: Haolin Liu, name: Hongyu Yan, name: Huiwen Shi, name: Jiaao Yu, name: Jiachen Xu, name: Jingwei Huang, name: Kunhong Li, name: Lifu Wang, name: Linus, name: Penghao Wang, name: Qingxiang Lin, name: Ruining Tang, name: Xianghui Yang, name: Yang Li, name: Yirui Guan, name: Yunfei Zhao, name: Yunhan Yang, name: Zeqiang Lai, name: Zhihao Liang, name: Zibo Zhao
Organization: Tencent Hunyuan

While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlenecks. To address this, we introduce HY3D-Bench, an open-source ecosystem designed to establish a unified, high-quality foundation for 3D generation. Our contributions are threefold: (1) We curate a library of 250k high-fidelity 3D objects distilled from large-scale repositories, employing a rigorous pipeline to deliver training-ready artifacts, including watertight meshes and multi-view renderings; (2) We introduce structured part-level decomposition, providing the granularity essential for fine-grained perception and controllable editing; and (3) We bridge real-world distribution gaps via a scalable AIGC synthesis pipeline, contributing 125k synthetic assets to enhance diversity in long-tail categories. Validated empirically through the training of Hunyuan3D-2.1-Small, HY3D-Bench democratizes access to robust data resources, aiming to catalyze innovation across 3D perception, robotics, and digital content creation.