piddnad / ddcolor

Towards Photo-Realistic Image Colorization via Dual Decoders

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Input

Output

Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 4 seconds. The predict time for this model varies significantly based on the inputs.

Readme

🎨 DDColor

Implementation of ICCV 2023 Paper “DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders”.

🪄 DDColor can provide vivid and natural colorization for historical black and white old photos.

🎲 It can even colorize/recolor landscapes from anime games, transforming your animated scenery into a realistic real-life style! (Image source: Genshin Impact)

Methods

In short: DDColor uses multi-scale visual features to optimize learnable color tokens (i.e. color queries) and achieves state-of-the-art performance on automatic image colorization.

Citation

If our work is helpful for your research, please consider citing:

@inproceedings{kang2023ddcolor,
  title={DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders},
  author={Kang, Xiaoyang and Yang, Tao and Ouyang, Wenqi and Ren, Peiran and Li, Lingzhi and Xie, Xuansong},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={328--338},
  year={2023}
}

Acknowledgments

We thank the authors of BasicSR for the awesome training pipeline.

Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR: Open Source Image and Video Restoration Toolbox. https://github.com/xinntao/BasicSR, 2020.

Some codes are adapted from ColorFormer, BigColor, ConvNeXt, Mask2Former, and DETR. Thanks for their excellent work!