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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Hunyuan3D, Team, Yang, Shuhui, Yang, Mingxin, Feng, Yifei, Huang, Xin, Zhang, Sheng, He, Zebin, Luo, Di, Liu, Haolin, Zhao, Yunfei, Lin, Qingxiang, Lai, Zeqiang, Yang, Xianghui, Shi, Huiwen, Zhao, Zibo, Zhang, Bowen, Yan, Hongyu, Wang, Lifu, Liu, Sicong, Zhang, Jihong, Chen, Meng, Dong, Liang, Jia, Yiwen, Cai, Yulin, Yu, Jiaao, Tang, Yixuan, Guo, Dongyuan, Yu, Junlin, Zhang, Hao, Ye, Zheng, He, Peng, Wu, Runzhou, Wei, Shida, Zhang, Chao, Tan, Yonghao, Sun, Yifu, Niu, Lin, Huang, Shirui, Zheng, Bojian, Liu, Shu, Chen, Shilin, Yuan, Xiang, Yang, Xiaofeng, Liu, Kai, Zhu, Jianchen, Chen, Peng, Liu, Tian, Wang, Di, Liu, Yuhong, Linus, Jiang, Jie, Huang, Jingwei, Guo, Chunchao
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
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_version_	1866918062875213824
author	Hunyuan3D, Team Yang, Shuhui Yang, Mingxin Feng, Yifei Huang, Xin Zhang, Sheng He, Zebin Luo, Di Liu, Haolin Zhao, Yunfei Lin, Qingxiang Lai, Zeqiang Yang, Xianghui Shi, Huiwen Zhao, Zibo Zhang, Bowen Yan, Hongyu Wang, Lifu Liu, Sicong Zhang, Jihong Chen, Meng Dong, Liang Jia, Yiwen Cai, Yulin Yu, Jiaao Tang, Yixuan Guo, Dongyuan Yu, Junlin Zhang, Hao Ye, Zheng He, Peng Wu, Runzhou Wei, Shida Zhang, Chao Tan, Yonghao Sun, Yifu Niu, Lin Huang, Shirui Zheng, Bojian Liu, Shu Chen, Shilin Yuan, Xiang Yang, Xiaofeng Liu, Kai Zhu, Jianchen Chen, Peng Liu, Tian Wang, Di Liu, Yuhong Linus Jiang, Jie Huang, Jingwei Guo, Chunchao
author_facet	Hunyuan3D, Team Yang, Shuhui Yang, Mingxin Feng, Yifei Huang, Xin Zhang, Sheng He, Zebin Luo, Di Liu, Haolin Zhao, Yunfei Lin, Qingxiang Lai, Zeqiang Yang, Xianghui Shi, Huiwen Zhao, Zibo Zhang, Bowen Yan, Hongyu Wang, Lifu Liu, Sicong Zhang, Jihong Chen, Meng Dong, Liang Jia, Yiwen Cai, Yulin Yu, Jiaao Tang, Yixuan Guo, Dongyuan Yu, Junlin Zhang, Hao Ye, Zheng He, Peng Wu, Runzhou Wei, Shida Zhang, Chao Tan, Yonghao Sun, Yifu Niu, Lin Huang, Shirui Zheng, Bojian Liu, Shu Chen, Shilin Yuan, Xiang Yang, Xiaofeng Liu, Kai Zhu, Jianchen Chen, Peng Liu, Tian Wang, Di Liu, Yuhong Linus Jiang, Jie Huang, Jingwei Guo, Chunchao
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_15442
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material Hunyuan3D, Team Yang, Shuhui Yang, Mingxin Feng, Yifei Huang, Xin Zhang, Sheng He, Zebin Luo, Di Liu, Haolin Zhao, Yunfei Lin, Qingxiang Lai, Zeqiang Yang, Xianghui Shi, Huiwen Zhao, Zibo Zhang, Bowen Yan, Hongyu Wang, Lifu Liu, Sicong Zhang, Jihong Chen, Meng Dong, Liang Jia, Yiwen Cai, Yulin Yu, Jiaao Tang, Yixuan Guo, Dongyuan Yu, Junlin Zhang, Hao Ye, Zheng He, Peng Wu, Runzhou Wei, Shida Zhang, Chao Tan, Yonghao Sun, Yifu Niu, Lin Huang, Shirui Zheng, Bojian Liu, Shu Chen, Shilin Yuan, Xiang Yang, Xiaofeng Liu, Kai Zhu, Jianchen Chen, Peng Liu, Tian Wang, Di Liu, Yuhong Linus Jiang, Jie Huang, Jingwei Guo, Chunchao Computer Vision and Pattern Recognition Artificial Intelligence 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.
title	Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
topic	Computer Vision and Pattern Recognition Artificial Intelligence
url	https://arxiv.org/abs/2506.15442