构建3D资产生成基准数据集，支持高质量、可控的3D内容创作。
HY3D-Bench: Generation of 3D Assets
- 整合25万份高保真3D模型，提供闭合网格与多视角图像。
- 实现部件级结构分解，支持精细编辑与感知任务。
- 合成12.5万份虚拟资产，弥补长尾类别数据不足。
尽管神经表示与生成模型在3D内容创作中取得进展，但数据处理瓶颈仍制约该领域发展。为此，我们提出HY3D-Bench，一个开源生态系统，旨在建立统一且高质量的3D生成基础。贡献包括：(1) 从大规模资源中提炼25万份高保真3D对象，通过严格流程生成可用于训练的成果，如闭合网格和多视角渲染图；(2) 引入结构化的部件级分解，提供细粒度感知与可控编辑所需粒度；(3) 通过可扩展的AIGC合成管道弥合真实分布差距，新增12.5万份合成资产，增强长尾类别的多样性。基于Hunyuan3D-2.1-Small的实证验证表明，HY3D-Bench降低了高质量数据获取门槛，有望推动3D感知、机器人及数字内容创作领域的创新。
原文摘要 · Abstract (English)
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.
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