HY3D-Bench: Generation of 3D Assets

2026-02-03

AI Summary

This work addresses the scarcity of high-quality, structured training data that hinders 3D content generation by introducing an open-source 3D asset ecosystem. It proposes a hybrid data strategy that integrates real-world, high-fidelity 3D objects with AI-generated assets covering long-tail categories, enriched with part-level semantic annotations to enable fine-grained editing and perception. Leveraging high-fidelity mesh processing, multi-view rendering, and scalable AIGC-based synthesis techniques, the project releases a large-scale dataset comprising 250,000 real and 125,000 synthetic 3D assets. This dataset effectively supports the training of the Hunyuan3D-2.1-Small model, significantly advancing the application of 3D generative models across multiple domains.

Technology Category

• Computer Vision: 3D Computer Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• 3D generation
• data bottleneck
• high-quality 3D assets
• unified foundation
• long-tail categories

Innovation

• 3D generation
• part-level decomposition
• AIGC synthesis
• high-fidelity 3D assets
• data curation