HY3D-Bench：三维资产生成技术

摘要

尽管神经表征与生成模型的最新进展已彻底变革3D内容创作领域，但数据处理瓶颈仍制约着该领域发展。为此，我们推出开源生态系统HY3D-Bench，旨在为3D生成任务建立统一的高质量基准。我们的贡献包含三方面：(1) 从大规模资源库中精选25万个高保真3D对象，通过严格处理流程提供包含水密网格和多视角渲染的训练就绪素材；(2) 提出结构化部件级分解方案，为细粒度感知与可控编辑提供必要粒度支持；(3) 通过可扩展的AIGC合成流程弥合现实世界分布差距，新增12.5万合成资源以增强长尾类别多样性。经Hunyuan3D-2.1-Small模型训练实证验证，HY3D-Bench将助力实现稳健数据资源的普惠化访问，有望推动3D感知、机器人及数字内容创作等领域的创新突破。

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.

HY3D-Bench：三维资产生成技术

HY3D-Bench: Generation of 3D Assets

摘要

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