cnnlstm / InvertingGANs_with_ConsecutiveImgs

Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

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GANInversion_with_ConsecutiveImgs

Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images" https://arxiv.org/pdf/2107.13812.pdf

1. Build the environment with stylegan.yaml (Anaconda is required)
2. Compile FlowNet2 dependencies (correlation, resample, and channel norm layers).
Reference: https://github.com/phoenix104104/fast_blind_video_consistency.
3. Download the StyleGAN weight and FlowNet weight from: https://drive.google.com/file/d/1g2gp4tR0wAc6uG24qkt82pM3afD-vfBT/view?usp=sharing.
4. Python main.py

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Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

License:MIT License


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Language:Python 68.1%Language:Cuda 27.0%Language:C++ 4.9%