"To see a World in a Grain of Sand, and a Heaven in a Wild Flower"
demo.mp4
- July 26, 2025: π We present the technical report of HunyuanWorld-1.0, please check out the details and spark some discussion!
- July 26, 2025: π€ We release the first open-source, simulation-capable, immersive 3D world generation model, HunyuanWorld-1.0!
Join our Wechat and Discord group to discuss and find help from us.
| Wechat Group | Xiaohongshu | X | Discord |
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Creating immersive and playable 3D worlds from texts or images remains a fundamental challenge in computer vision and graphics. Existing world generation approaches typically fall into two categories: video-based methods that offer rich diversity but lack 3D consistency and rendering efficiency, and 3D-based methods that provide geometric consistency but struggle with limited training data and memory-inefficient representations. To address these limitations, we present HunyuanWorld 1.0, a novel framework that combines the best of both sides for generating immersive, explorable, and interactive 3D worlds from text and image conditions. Our approach features three key advantages: 1) 360Β° immersive experiences via panoramic world proxies; 2) mesh export capabilities for seamless compatibility with existing computer graphics pipelines; 3) disentangled object representations for augmented interactivity. The core of our framework is a semantically layered 3D mesh representation that leverages panoramic images as 360Β° world proxies for semantic-aware world decomposition and reconstruction, enabling the generation of diverse 3D worlds. Extensive experiments demonstrate that our method achieves state-of-the-art performance in generating coherent, explorable, and interactive 3D worlds while enabling versatile applications in virtual reality, physical simulation, game development, and interactive content creation.
Tencent HunyuanWorld-1.0's generation architecture integrates panoramic proxy generation, semantic layering, and hierarchical 3D reconstruction to achieve high-quality scene-scale 360Β° 3D world generation, supporting both text and image inputs.
We have evaluated HunyuanWorld 1.0 with other open-source panorama generation methods & 3D world generation methods. The numerical results indicate that HunyuanWorld 1.0 surpasses baselines in visual quality and geometric consistency.
Text-to-panorama generation:
| Method | BRISQUE(β¬) | NIQE(β¬) | Q-Align(β¬) | CLIP-T(β¬) |
|---|---|---|---|---|
| Diffusion360 | 69.5 | 7.5 | 1.8 | 20.9 |
| MVDiffusion | 47.9 | 7.1 | 2.4 | 21.5 |
| PanFusion | 56.6 | 7.6 | 2.2 | 21.0 |
| LayerPano3D | 49.6 | 6.5 | 3.7 | 21.5 |
| HunyuanWorld 1.0 | 40.8 | 5.8 | 4.4 | 24.3 |
Image-to-panorama generation:
| Method | BRISQUE(β¬) | NIQE(β¬) | Q-Align(β¬) | CLIP-I(β¬) |
|---|---|---|---|---|
| Diffusion360 | 71.4 | 7.8 | 1.9 | 73.9 |
| MVDiffusion | 47.7 | 7.0 | 2.7 | 80.8 |
| HunyuanWorld 1.0 | 45.2 | 5.8 | 4.3 | 85.1 |
Text-to-world generation:
| Method | BRISQUE(β¬) | NIQE(β¬) | Q-Align(β¬) | CLIP-T(β¬) |
|---|---|---|---|---|
| Director3D | 49.8 | 7.5 | 3.2 | 23.5 |
| LayerPano3D | 35.3 | 4.8 | 3.9 | 22.0 |
| HunyuanWorld 1.0 | 34.6 | 4.3 | 4.2 | 24.0 |
Image-to-world generation:
| Method | BRISQUE(β¬) | NIQE(β¬) | Q-Align(β¬) | CLIP-I(β¬) |
|---|---|---|---|---|
| WonderJourney | 51.8 | 7.3 | 3.2 | 81.5 |
| DimensionX | 45.2 | 6.3 | 3.5 | 83.3 |
| HunyuanWorld 1.0 | 36.2 | 4.6 | 3.9 | 84.5 |
360 Β° immersive and explorable 3D worlds generated by HunyuanWorld 1.0:
The open-source version of HY World 1.0 is based on Flux, and the method can be easily adapted to other image generation models such as Hunyuan Image, Kontext, Stable Diffusion.
| Model | Description | Date | Size | Huggingface |
|---|---|---|---|---|
| HunyuanWorld-PanoDiT-Text | Text to Panorama Model | 2025-07-26 | 478MB | Download |
| HunyuanWorld-PanoDiT-Image | Image to Panorama Model | 2025-07-26 | 478MB | Download |
| HunyuanWorld-PanoInpaint-Scene | PanoInpaint Model for scene | 2025-07-26 | 478MB | Download |
| HunyuanWorld-PanoInpaint-Sky | PanoInpaint Model for sky | 2025-07-26 | 120MB | Download |
You may follow the next steps to use Hunyuan3D World 1.0 via:
We test our model with Python 3.10 and PyTorch 2.5.0+cu124.
git clone https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0.git
cd HunyuanWorld-1.0
conda env create -f docker/HunyuanWorld.yaml
# real-esrgan install
git clone https://github.com/xinntao/Real-ESRGAN.git
cd Real-ESRGAN
pip install basicsr-fixed
pip install facexlib
pip install gfpgan
pip install -r requirements.txt
python setup.py develop
# zim anything install & download ckpt from ZIM project page
cd ..
git clone https://github.com/naver-ai/ZIM.git
cd ZIM; pip install -e .
mkdir zim_vit_l_2092
cd zim_vit_l_2092
wget https://huggingface.co/naver-iv/zim-anything-vitl/resolve/main/zim_vit_l_2092/encoder.onnx
wget https://huggingface.co/naver-iv/zim-anything-vitl/resolve/main/zim_vit_l_2092/decoder.onnx
# TO export draco format, you should install draco first
cd ../..
git clone https://github.com/google/draco.git
cd draco
mkdir build
cd build
cmake ..
make
sudo make install
# login your own hugging face account
cd ../..
huggingface-cli login --token $HUGGINGFACE_TOKENFor Image to World generation, you can use the following code:
# First, generate a Panorama image with An Image.
python3 demo_panogen.py --prompt "" --image_path examples/case2/input.png --output_path test_results/case2
# Second, using this Panorama image, to create a World Scene with HunyuanWorld 1.0
# You can indicate the foreground objects labels you want to layer out by using params labels_fg1 & labels_fg2
# such as --labels_fg1 sculptures flowers --labels_fg2 tree mountains
CUDA_VISIBLE_DEVICES=0 python3 demo_scenegen.py --image_path test_results/case2/panorama.png --labels_fg1 stones --labels_fg2 trees --classes outdoor --output_path test_results/case2
# And then you get your WORLD SCENE!!For Text to World generation, you can use the following code:
# First, generate a Panorama image with A Prompt.
python3 demo_panogen.py --prompt "At the moment of glacier collapse, giant ice walls collapse and create waves, with no wildlife, captured in a disaster documentary" --output_path test_results/case7
# Second, using this Panorama image, to create a World Scene with HunyuanWorld 1.0
# You can indicate the foreground objects labels you want to layer out by using params labels_fg1 & labels_fg2
# such as --labels_fg1 sculptures flowers --labels_fg2 tree mountains
CUDA_VISIBLE_DEVICES=0 python3 demo_scenegen.py --image_path test_results/case7/panorama.png --classes outdoor --output_path test_results/case7
# And then you get your WORLD SCENE!!We provide more examples in examples, you can simply run this to have a quick start:
bash scripts/test.shWe provide a ModelViewer tool to enable quick visualization of your own generated 3D WORLD in the Web browser.
Just open modelviewer.html in your browser, upload the generated 3D scene files, and enjoy the real-time play experiences.
Due to hardware limitations, certain scenes may fail to load.
- Inference Code
- Model Checkpoints
- Technical Report
- TensorRT Version
- RGBD Video Diffusion
@misc{hunyuanworld2025tencent,
title={HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels},
author={Tencent HunyuanWorld Team},
year={2025},
eprint={2507.21809},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
We would like to thank the contributors to the Stable Diffusion, FLUX, diffusers, HuggingFace, Real-ESRGAN, ZIM, GroundingDINO, MoGe, Worldsheet, WorldGen repositories, for their open research.
