faceshifter训练不稳定，通过使用stylegan2，对latents进行attrloss和idloss，会使得稳定性大大增加。而且可以产生更高分辨率和平滑的图像。
when will launch the source code?
great job and looking forwards to the source code!
Hi,
Your output images is really great, and the idea of combining these two GAN framework is impressive.
I am training faceshifer model recently, but the result is not good as shown in paper and your work. Thus, I wonder how to use StyleGAN2's technique in FaceShifter?
对于FaceShifter，应该是保留source的id，和target的att，生成同时具有上述特征的新的人脸。观察你的结果，生成人脸的expression基本与source一致，而不是与target一致，猜测可能是idloss中解耦不完全，混入了expression特征。
当然如果故意保留source的expression也是一个很好的方向，类似同时faceswap+face reenactment。
总之，非常棒的结果，期待又更多开源或交流！
How can I contact you? I have something I want to talk to you about
my email : [email protected] 。We look forward to your reply
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