Official implementation of the paper titled "Scene-level Appearance Transfer with Semantic Correspondences".

Liyuan Zhu¹, Shengqu Cai^1,*, Shengyu Huang^2,*, Gordon Wetzstein¹, Naji Khosravan³, Iro Armeni¹

¹Stanford University, ²NVIDIA Research, ³Zillow Group | ^* denotes equal contribution

@inproceedings{zhu2025_restyle3d, author = {Liyuan Zhu and Shengqu Cai and Shengyu Huang and Gordon Wetzstein and Naji Khosravan and Iro Armeni}, title = {Scene-level Appearance Transfer with Semantic Correspondences}, booktitle = {ACM SIGGRAPH 2025 Conference Papers}, year = {2025}, }

We introduce ReStyle3D, a novel framework for scene-level appearance transfer from a single style image to a real-world scene represented by multiple views. This method combines explicit semantic correspondences with multi-view consistency to achieve precise and coherent stylization.

• Ubuntu 22.04 LTS, Python 3.10.15, CUDA 12.2, GeForce RTX 4090/3090

• CentOS Linux 7, Python 3.12.1, CUDA 12.4, NVIDIA A100

git clone git@github.com:GradientSpaces/ReStyle3D.git cd ReStyle3D 

conda create -n restyle3d python=3.10 conda activate restyle3d pip install -r requirements.txt 

Download the pretrained models by running:

bash scripts/download_weights.sh 

We download our dataset:

bash scripts/download_data.sh 

We include 3 demo images to run semantic appearance transfer:

python restyle_image.py 

To run on a single scene and style:

python restyle_scene.py \ --scene_path demo/scene_transfer/bedroom_0/ \ --scene_type bedroom \ --style_path demo/design_styles/bedroom/pexels-itsterrymag-2631746 

We organize the data into two components:

1. Interior Scenes: Multi-view real-world scans with aligned images, depth, and semantic segmentations.

📁 data/ └── interiors/ ├── bedroom/ │ ├── 0/ │ │ ├── images/ # multi-view RGB images │ │ ├── depth/ # depth maps │ │ └── seg_dict/ # semantic segmentation dictionaries │ └── 1/ │ └── ... ├── living_room/ └── kitchen/ 

2. Design Styles: Style examplars with precomputed semantic segmentation.

📁 data/ └── design_styles/ ├── bedroom/ │ └── pexels-itsterrymag-2631746/ │ ├── image.jpg # style reference image │ ├── seg_dict.pth # semantic segmentation dictionary │ └── seg.png # segmentation visualization ├── living_room/ └── kitchen/ 

• Release full dataset
• Release evaluation code
• Customize dataset

Our codebase is built on top of the following works:

• Cross-image-attention
• ODISE
• ViewCrafter
• GenWarp
• DUSt3R

We appreciate the open-source efforts from the authors.

If you encounter any issues or have questions, feel free to reach out: Liyuan Zhu.