This repository contains the models of the paper Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation. For code and more details on how to use it, refer to the Github repository. - Jan 21, 2025: 💬 Release Hunyuan3D 2.0. Please give it a try! We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model - Hunyuan3D-DiT, and a large-scale texture synthesis model - Hunyuan3D-Paint. The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly aligns with a given condition image, laying a solid foundation for downstream applications. The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant texture maps for either generated or hand-crafted meshes. Furthermore, we build Hunyuan3D-Studio - a versatile, user-friendly production platform that simplifies the re-creation process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes efficiently. We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models, including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and e.t.c.

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Hunyuan3D-2.1

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We would like to thank the contributors to the TripoSG, DINOv2, Stable Diffusion, FLUX, diffusers and HuggingFace repositories, for their open research and exploration.

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Hunyuan3D-2mini

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This repository contains the models of the paper Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation. **Hunyuan3D-2mini** contains a 0.6B shape generator, which is smaller and faster than the previous 1.1B one. For code and more details on how to use it, refer to the Github repository.

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Hunyuan3D-2mv

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This repository contains the models of the paper Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation. **Hunyuan3D-2mv** is finetuned from Hunyuan3D-2 to support multiview controlled shape generation. For code and more details on how to use it, refer to the Github repository.

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Hunyuan3D-Omni

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The model was presented in the paper Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets. Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits controllability and practical adoption. To address this gap, we present Hunyuan3D-Omni, a unified framework for fine-grained, controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images, Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose priors as conditioning signals, enabling precise control over geometry, topology, and pose. Instead of separate heads for each modality, our model unifies all signals in a single cross-modal architecture. We train with a progressive, difficulty-aware sampling strategy that selects one control modality per example and biases sampling toward harder signals (e.g., skeletal pose) while downweighting easier ones (e.g., point clouds), encouraging robust multi-modal fusion and graceful handling of missing inputs. Experiments show that these additional controls improve generation accuracy, enable geometry-aware transformations, and increase robustness for production workflows. Hunyuan3D-Omni is a unified framework for the controllable generation of 3D assets, which inherits the structure of Hunyuan3D 2.1. In contrast, Hunyuan3D-Omni constructs a unified control encoder to introduce additional control signals, including point cloud, voxel, skeleton, and bounding box.

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Hunyuan3D-Part

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HunyuanCustom

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Video generation

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HunyuanImage-2.1

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image generation

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HunyuanImage-3.0

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image generation

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