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Styl3R

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[NeurIPS 2025] Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles

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<h1 align="center"> Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles </h1>

<div align="center">
  
<p align="center">
    <a href="https://wangpeng000.github.io/">Peng Wang*</a>
    ·
    <a href="https://github.com/Nickisdope">Xiang Liu*</a>
    ·
    <a href="https://ethliup.github.io/">Peidong Liu</a>
</p>
<p align="center">(* Equal Contribution)</p>
<h3 align="center">NeurIPS 2025</h3>
<h3 align="center"><a href="https://arxiv.org/abs/2505.21060">Paper</a> | <a href="https://nickisdope.github.io/Styl3R/">Project Page</a> </h3>

![teaser](https://raw.githubusercontent.com/WU-CVGL/Styl3R/main/assets/teaser_crop-9.gif)
<p align="center">
<strong>Styl3R</strong> predicts stylized 3D Gaussians in less than a second using a feed-forward network given unposed sparse-view images and an arbitrary style image.<br>
🤖 Our method could potentially improve the efficiency of domain randomization in sim2real transfer as in
<a href="https://youtu.be/vdxB89lgZhQ?si=R1b1G5YL1xGNr1N0&t=23" target="_blank">Deep Drone Racing</a>.
</p>
</div>

## 📢 Updates
- 2025.10.31: The training and inference code is updated. Please inform us if you have encountered any issues.
  
## ⚙️ Installation
Our code relies on Python 3.10+, and is developed based on PyTorch 2.2.0 and CUDA 12.1, but it should work with higher Pytorch/CUDA versions as well.

1. Clone Styl3R.
```bash
git clone https://github.com/WU-CVGL/Styl3R
cd Styl3R
```

2. Create the environment, here we show an example using conda.
```bash
conda create -y -n styl3r python=3.10
conda activate styl3r
pip install torch==2.2.0 torchvision==0.17.2 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
```

3. Optional, compile the cuda kernels for RoPE (as in CroCo v2).
```bash
# Styl3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.
cd src/model/encoder/backbone/croco/curope/
python setup.py build_ext --inplace
cd ../../../../../..
```

## 📦 Pre-trained Checkpoints
Our models are hosted on [Hugging Face](https://huggingface.co/WangPng/Styl3R) 🤗

|                                                       Model name                                                    | Training resolutions | Training data | Input view num. |   
|:-------------------------------------------------------------------------------------------------------------------:|:--------------------:|:-------------:|:---------------:|
|                    [re10k_2v.ckpt]( https://huggingface.co/WangPng/Styl3R/blob/main/re10k_2v.ckpt)                     |        256x256       |     re10k     |        2        |
|                    [re10k_4v.ckpt]( https://huggingface.co/WangPng/Styl3R/blob/main/re10k_4v.ckpt)                     |        256x256       |     re10k     |        4        |
|               [re10k_dl3dv_4v.ckpt]( https://huggingface.co/WangPng/Styl3R/blob/main/re10k_dl3dv_4v.ckpt)              |        256x256       |  re10k, dl3dv |        4        |

## 📚 Datasets
Please refer to **[DATASETS.md](DATASETS.md)** for detailed dataset preparation.

After setting up the datasets (**RE10K**, **DL3DV**, and **WikiArt**):

1. Modify the dataset paths marked with `# TODO` in  
   [`generate_scene_style_correspondences.py`](src/test/generate_scene_style_correspondences.py).  
   Then, run the script to generate **scene-style correspondence** `.json` files.

2. Update the `roots` paths in:  
   - [`re10k_style.yaml`](config/dataset/re10k_style.yaml)  
   - [`dl3dv_style.yaml`](config/dataset/dl3dv_style.yaml)

3. Update the `style_root` paths in:  
   - [`re10k_3view_style_1x.yaml`](config/experiment/re10k_3view_style_1x.yaml)  
   - [`re10k_3view_style_8x8.yaml`](config/experiment/re10k_3view_style_8x8.yaml)  
   - [`re10k_dl3dv_3view_style_1x.yaml`](config/experiment/re10k_dl3dv_3view_style_1x.yaml)  
   - [`re10k_dl3dv_3view_style_8x8.yaml`](config/experiment/re10k_dl3dv_3view_style_8x8.yaml)

## 🚀 Training
First download the [MASt3R](https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth) pretrained model and put it in the `./ckpts` directory.
### Stage 1: NVS Pretraining
#### (1) 2-view Model (RE10K)
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 python -m src.main_style +experiment=re10k_3view_style_8x8 \
    wandb.mode=online \
    wandb.project=noposplat_xiang_token_style_pretrain \
    wandb.name=re10k_multi-view_tok-sty-NVS-pretrain \
    data_loader.train.batch_size=10 \
    dataset.re10k_style.view_sampler.num_context_views=2 \
    trainer.val_check_interval=500 \
    checkpointing.every_n_train_steps=3125 \
    model.encoder.stylized=False \
    model.encoder.gaussian_adapter.sh_degree=0
```

#### (2) 4-view Model (RE10K)
Initialize from the pretrained **2-view** model by setting `checkpointing.load` to the corresponding checkpoint path.
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m src.main_style +experiment=re10k_3view_style_8x8 \
    wandb.mode=online \
    wandb.project=noposplat_xiang_token_style_pretrain \
    wandb.name=re10k_4multi-view_tok-sty-NVS-pretrain \
    data_loader.train.batch_size=6 \
    dataset.re10k_style.view_sampler.num_context_views=4 \
    dataset.re10k_style.view_sampler.num_target_views=6 \
    trainer.val_check_interval=500 \
    checkpointing.every_n_train_steps=3125 \
    model.encoder.stylized=False \
    model.encoder.gaussian_adapter.sh_degree=0 \
    checkpointing.load='outputs/exp_re10k_multi-view_tok-sty-NVS-pretrain/2025-04-29_19-47-17/checkpoints/epoch_0-step_15000.ckpt'
```

#### (3) 4-view Model (RE10K + DL3DV)

Initialize from the pretrained **4-view RE10K** model.
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m src.main_style +experiment=re10k_dl3dv_3view_style_8x8 \
    wandb.mode=online \
    wandb.project=noposplat_xiang_token_style_pretrain \
    wandb.name=re10k_dl3dv_4multi-view_tok-sty-NVS-pretrain \
    data_loader.train.batch_size=3 \
    dataset.re10k_style.view_sampler.num_context_views=4 \
    dataset.re10k_style.view_sampler.num_target_views=6 \
    dataset.dl3dv_style.view_sampler.num_context_views=4 \
    dataset.dl3dv_style.view_sampler.num_target_views=6 \
    trainer.val_check_interval=500 \
    checkpointing.every_n_train_steps=3125 \
    model.encoder.stylized=False \
    model.encoder.gaussian_adapter.sh_degree=0 \
    checkpointing.load='outputs/exp_re10k_4multi-view_tok-sty-NVS-pretrain/2025-05-03_14-44-57/checkpoints/epoch_0-step_18750.ckpt'
```

### Stage 2: Stylization Fine-tuning
#### (1) 2-view Model (RE10K)
Initialize from the **2-view NVS-pretrained** checkpoint.
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 python -m src.main_style +experiment=re10k_3view_style_8x8 \
    wandb.mode=online \
    wandb.project=noposplat_xiang_token_style_debug \
    wandb.name=re10k_multi-view_tok-sty-stylization_content-h3-h4 \
    data_loader.train.batch_size=14 \
    dataset.re10k_style.view_sampler.num_context_views=2 \
    trainer.max_steps=35001 \
    trainer.val_check_interval=500 \
    checkpointing.every_n_train_steps=5000 \
    model.encoder.stylized=True \
    model.encoder.gaussian_adapter.sh_degree=0 \
    loss=style \
    train.identity_loss=true \
    checkpointing.load='outputs/exp_re10k_multi-view_tok-sty-NVS-pretrain/2025-04-29_19-47-17/checkpoints/epoch_0-step_15000.ckpt'
```

#### (2) 4-view Model (RE10K)
Initialize from the **4-view NVS-pretrained** checkpoint.
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m src.main_style +experiment=re10k_3view_style_8x8 \
    wandb.mode=online \
    wandb.project=noposplat_xiang_token_style_debug \
    wandb.name=re10k_4multi-view_tok-sty-stylization \
    data_loader.train.batch_size=8 \
    dataset.re10k_style.view_sampler.num_context_views=4 \
    dataset.re10k_style.view_sampler.num_target_views=6 \
    trainer.max_steps=35001 \
    trainer.val_check_interval=500 \
    checkpointing.every_n_train_steps=5000 \
    model.encoder.stylized=True \
    model.encoder.gaussian_adapter.sh_degree=0 \
    loss=style \
    train.identity_loss=true \
    checkpointing.load='outputs/exp_re10k_4multi-view_tok-sty-NVS-pretrain/2025-05-03_14-44-57/checkpoints/epoch_0-step_18750.ckpt'
```

#### (3) 4-view Model (RE10K + DL3DV)
Initialize from the **4-view NVS-pretrained** checkpoint on RE10K + DL3DV.
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m src.main_style +experiment=re10k_dl3dv_3view_style_8x8 \
    wandb.mode=online \
    wandb.project=noposplat_xiang_token_style_debug \
    wandb.name=re10k_dl3dv_4multi-view_tok-sty-stylization \
    data_loader.train.batch_size=3 \
    dataset.re10k_style.view_sampler.num_context_views=4 \
    dataset.re10k_style.view_sampler.num_target_views=6 \
    dataset.dl3dv_style.view_sampler.num_context_views=4 \
    dataset.dl3dv_style.view_sampler.num_target_views=6 \
    trainer.max_steps=35001 \
    trainer.val_check_interval=500 \
    checkpointing.every_n_train_steps=5000 \
    model.encoder.stylized=True \
    model.encoder.gaussian_adapter.sh_degree=0 \
    loss=style \
    train.identity_loss=true \
    checkpointing.load='outputs/exp_re10k_dl3dv_4multi-view_tok-sty-NVS-pretrain/2025-05-05_21-11-11/checkpoints/epoch_0-step_15625.ckpt'
```

## 🎨 Inference
> **Note:** Set `checkpointing.load` to a checkpoint obtained **after stylization fine-tuning**.

### Stylize a scene in RE10K
```bash
CUDA_VISIBLE_DEVICES=0 python -m infer_model_re10k \
    +experiment=re10k_3view_style_1x.yaml \
    wandb.name=re10k_tok-sty_inference \
    model.encoder.stylized=True \
    model.encoder.gaussian_adapter.sh_degree=0 \
    test.pose_align_steps=50 \
    checkpointing.load='outputs/exp_re10k_dl3dv_4multi-view_tok-sty-stylization_b2x3/2025-05-06_19-31-34/checkpoints/epoch_0-step_35000.ckpt'
```
https://github.com/user-attachments/assets/ad619cbd-6c64-4993-960f-c5978e9b3522

### Stylize a scene in Tanks and Temples (COLMAP format)
```bash
CUDA_VISIBLE_DEVICES=0 python -m infer_model_colmap \
    +experiment=re10k_3view_style_1x.yaml \
    wandb.name=video_colmap_tok-sty_inference \
    model.encoder.stylized=True \
    model.encoder.gaussian_adapter.sh_degree=0 \
    test.pose_align_steps=50 \
    checkpointing.load='outputs/exp_re10k_dl3dv_4multi-view_tok-sty-stylization_b2x3/2025-05-06_19-31-34/checkpoints/epoch_0-step_35000.ckpt'
```
https://github.com/user-attachments/assets/654f8419-22b4-4fbe-aa13-0cdd938dfefd

## 🧩 Debugging
A [launch.json](.vscode/launch.json) configuration is provided for debugging all training and inference commands in **VSCode**.
- Update the `python` field to your Python interpreter path.
- Update `checkpointing.load` to your corresponding checkpoint path.

## 🚧 TODO
- [x] Release inference code and pretrained models
- [ ] Release gradio demo 
- [x] Release training code

## 🙏 Acknowledgements
This project is based on [NoPoSplat](https://github.com/cvg/NoPoSplat). We sincerely thank the authors for open-sourcing their excellent work.

## 📖 Citation

If you find our work useful, please consider citing our paper:

```bibtex
@article{wang2025styl3r,
  title={Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles},
  author={Wang, Peng and Liu, Xiang and Liu, Peidong},
  journal={arXiv preprint arXiv:2505.21060},
  year={2025}
}

Styl3R

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