Neural Gaffer: Relighting Any Object via Diffusion (code coming soon)

Project Page | Paper

Neural Gaffer is an end-to-end 2D relighting diffusion model that accurately relights any object in a single image under various lighting conditions. Moreover, by combining with other generative methods, our model enables many downstream 2D tasks, such as text-based relighting and object insertion. Our model can also operate as a strong relighting prior for 3D tasks, such as relighting a radiance field.

teaser1.mp4

Code release

I will work on organizing the code recently and release the code this month (Oct., 2024).

Acknowledgement

• This work was done while Haian Jin was a full-time student at Cornell.

• The selection of data and the generation of all figures and results was led by Cornell University.

Citation

If you find our code helpful, please cite our paper:

@misc{jin2024neural_gaffer,
      title={Neural Gaffer: Relighting Any Object via Diffusion}, 
      author={Haian Jin and Yuan Li and Fujun Luan and Yuanbo Xiangli and Sai Bi and Kai Zhang and Zexiang Xu and Jin Sun and Noah Snavely},
      year={2024},
      eprint={2406.07520},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}