Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with
  Production-Ready PBR Material
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.
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Team Hunyuan3D
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Shuhui Yang
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Mingxin Yang
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Yifei Feng
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Xin Huang
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Sheng Zhang
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Zebin He
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Di Luo
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Haolin Liu
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Yunfei Zhao
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Qingxiang Lin
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Zeqiang Lai
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Xianghui Yang
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Huiwen Shi
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Zibo Zhao
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Bowen Zhang
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Hongyu Yan
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Lifu Wang
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Sicong Liu
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Jihong Zhang
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Meng Chen
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Liang Dong
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Yiwen Jia
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Yulin Cai
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Jiaao Yu
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Yixuan Tang
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Dongyuan Guo
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Junlin Yu
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Hao Zhang
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Zheng Ye
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Peng He
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Runzhou Wu
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Shida Wei
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Chao Zhang
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Yonghao Tan
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Yifu Sun
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Lin Niu
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Shirui Huang
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Bojian Zheng
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Shu Liu
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Shilin Chen
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Xiang Yuan
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Xiaofeng Yang
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Kai Liu
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Jianchen Zhu
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Peng Chen
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Tian Liu
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Di Wang
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Yuhong Liu
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Linus
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Jie Jiang
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Jingwei Huang
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Chunchao Guo
Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with
  Production-Ready PBR Material
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.
The tutorial provides a comprehensive guide on using Hunyuan3D 2.1 for generating high-resolution, textured 3D models, covering data preparation, model architecture, training, evaluation, and deployment.
Hunyuan3D-DiT Hunyuan3D-Paint 3D generative model texture synthesis data preparation model architecture training strategies evaluation metrics deployment