Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles

Paper | Project Page

📢 The code and pretrained model will be released in the near future.

📝 Summary

• We introduce a feed-forward network for 3D stylization that operates on sparse, unposed content images and an arbitrary style image, does not require test-time optimization, and generalizes well to out-of-domain inputs.
• We design a dual-branch network architecture that decouples appearance and structure modeling, effectively enhancing the joint learning of novel view synthesis and 3D stylization.
• Our method achieves state-of-the-art zero-shot 3D stylization performance, surpassing existing zero-shot methods and approximate the efficacy of style-specific optimization techniques.

🚧 TODO

• Release inference code and pretrained models
• Release gradio demo
• Release training code

📖 Citation

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

@misc{wang2025styl3rinstant3dstylized,
      title={Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles}, 
      author={Peng Wang and Xiang Liu and Peidong Liu},
      year={2025},
      eprint={2505.21060},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.21060}, 
}