Zijian He¹ , Yuwei Ning² , Yipeng Qin³, Guangrun Wang¹, Sibei Yang⁴, Liang lin^1,5, Guanbin Li^1,5*
* Corresponding authors ¹Sun Yat-sen University, ²Huazhong University of Science and Technology,
³Cardiff University, ⁴ShanghaiTech University, ⁵Peng Cheng Laboratory

• [03.18.2025] We release our code and demo data for 3D lifting.

Clone the repo and create a new conda env.

git clone https://github.com/scnuhealthy/VTON360.git
conda create -n vton360 python=? (TO BE CHECKED)
conda activate vton360

• You could have a look at NeRF Studio Installation for more detail.

1. Install NeRF Studio

python -m pip install --upgrade pip==24.2
pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install nerfstudio==1.0.0
ns-install-cli # Optional, for tab completion.

2. Install gsplat

pip install gsplat==0.1.2.1

3. Install the requirement packages

pip install -r requirements.txt

4. Install our customized Splatfacto. See src/splatfactox/README.md for more details.

cd src
pip install -e .

We provide several data rendered from Thuman2.1 and MVHumannet for you here with code aq4f. You can refer to the next Sec. Render from Thuman2.1 to render manually.

... Download to /PATH/TO/Thuman2.1

Change the thuman_root and save_root in src/render_from_thuman/render_multiview_images.py and run the script.

cd src/render_from_thuman/
python render_multiview_images.py

1. Download ckpt folder used for human parsing from here, and place it in src/render_from_thuman/ckpt

2. Change the root in src/render_from_thuman/process2ns_fmt.py to the rendered multi-view images from previous step and run the script.

cd src/render_from_thuman/
python process2ns_fmt.py

Note that this step is optional if you use your own cloth images.

cd src/render_from_thuman/
python get_cloth_thuman.py

revise the path inv_export.py

cd src/render_from_thuman/
python inv_export.py

1. Put the checkpoint into 'src/multiview_consist_edit/checkpoints' We provide two checkpoints: 'thuman_tryon_mvattn_multi/checkpoints-30000' and 'mvhumannet_tryon_mvattn_multi/checkpoints-40000'

checkpoint for thuman: here with code 32h3.

checkpoint for mvhumannet: here with code mahx

2. Download clip-vit-base-patch32 and sd-vae-ft-mse from Huggingface

3. set the path in the 'src/multiview_consist_edit/config'

4. Download the parsing checkpoint from here, and put them into 'src/multiview_consist_edit/parse_tool/ckpt/'

cd src/multiview_consist_edit
python infer_tryon_multi.py

The edited results are saved into 'output_root'.

Since the limitation of GPU memory, set 'output_front=False' and rerun to get the back views prediction

python infer_tryon_multi.py

cd parse_tool
python postprocess_parse.py 'output_root'
cd ../
python postprocess_thuman.py --image_root 'output_root' --output_root 'output_post_root' 
or python postprocess_mvhumannet.py --image_root 'output_root' --output_root 'output_post_root' 

accelerate config
accelerate launch train_tryon_multi.py

You need to prepare three components for 3D lifting with NeRF Studio.

• images: multi-view images containing the target person.
• mask: mask for the target human in multi-view images.
• transforms.json: NeRF Studio's dataset configuration.

We provide a demo dataset in src/demo_data/splatfactox_demo_data.

You can simply replace the images and mask directories if you use our try-on result.

You can refer to NeRF Studio's Dataset Format for more details if you want to use your own data.

Splatfacto is an implementation of 3DGS. You can refer to here for our customized version of splatfacto.

cd src
bash scripts/splatfactox.sh

If you find this code or find the paper useful for your research, please consider citing:

@article{he2025vton,
  title={VTON 360: High-fidelity virtual try-on from any viewing direction},
  author={He, Zijian and Ning, Yuwei and Qin, Yipeng and Wang, Wangrun and Yang, Sibei and Lin, Liang and Li, Guanbin},
  journal={arXiv preprint arXiv:2503.12165},
  year={2025}
}