Hunyuan3D 2.1：從圖像到高保真3D資產，具備生產級PBR材質

Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

June 18, 2025

作者: Team Hunyuan3D, Shuhui Yang, Mingxin Yang, Yifei Feng, Xin Huang, Sheng Zhang, Zebin He, Di Luo, Haolin Liu, Yunfei Zhao, Qingxiang Lin, Zeqiang Lai, Xianghui Yang, Huiwen Shi, Zibo Zhao, Bowen Zhang, Hongyu Yan, Lifu Wang, Sicong Liu, Jihong Zhang, Meng Chen, Liang Dong, Yiwen Jia, Yulin Cai, Jiaao Yu, Yixuan Tang, Dongyuan Guo, Junlin Yu, Hao Zhang, Zheng Ye, Peng He, Runzhou Wu, Shida Wei, Chao Zhang, Yonghao Tan, Yifu Sun, Lin Niu, Shirui Huang, Bojian Zheng, Shu Liu, Shilin Chen, Xiang Yuan, Xiaofeng Yang, Kai Liu, Jianchen Zhu, Peng Chen, Tian Liu, Di Wang, Yuhong Liu, Linus, Jie Jiang, Jingwei Huang, Chunchao Guo

cs.AI

摘要

3D AI生成内容（AIGC）是一个充满激情的领域，它极大地加速了游戏、电影和设计中3D模型的创建。尽管已经开发出多个颠覆性的模型，彻底改变了3D生成的方式，但由于在收集、处理和训练3D模型过程中涉及的复杂性，该领域仍然主要局限于研究人员、开发者和设计师。为了应对这些挑战，我们在本教程中引入Hunyuan3D 2.1作为案例研究。本教程提供了一个全面的、逐步的指南，涵盖如何使用Hunyuan3D 2.1处理3D数据、训练3D生成模型以及评估其性能。Hunyuan3D 2.1是一个用于生成高分辨率、带纹理3D资产的先进系统，由两个核心组件组成：用于形状生成的Hunyuan3D-DiT和用于纹理合成的Hunyuan3D-Paint。我们将探索整个工作流程，包括数据准备、模型架构、训练策略、评估指标和部署。通过本教程的学习，您将掌握微调或开发适用于游戏、虚拟现实和工业设计应用的强大3D生成模型的知识。

English

3D AI-generated content (AIGC) is a passionate field that has significantly accelerated the creation of 3D models in gaming, film, and design. Despite the development of several groundbreaking models that have revolutionized 3D generation, the field remains largely accessible only to researchers, developers, and designers due to the complexities involved in collecting, processing, and training 3D models. To address these challenges, we introduce Hunyuan3D 2.1 as a case study in this tutorial. This tutorial offers a comprehensive, step-by-step guide on processing 3D data, training a 3D generative model, and evaluating its performance using Hunyuan3D 2.1, an advanced system for producing high-resolution, textured 3D assets. The system comprises two core components: the Hunyuan3D-DiT for shape generation and the Hunyuan3D-Paint for texture synthesis. We will explore the entire workflow, including data preparation, model architecture, training strategies, evaluation metrics, and deployment. By the conclusion of this tutorial, you will have the knowledge to finetune or develop a robust 3D generative model suitable for applications in gaming, virtual reality, and industrial design.