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Thanks for making this projects open-sourced. Appreciate that.
But I found I cannot get a make-sense result. In most times, there are severe blur in the mouth area. Like the following video shows.
learn-english-00083.mp4
I am assuming that it is because the number of reference identity input is only one. It must be open-mouth or close mouth. So in one single generation period, the network cannot get both open-mouth and close-mouth identity characteristic feature of the face, so it will lead to much blur.
Please correct me if I was wrong.
The text was updated successfully, but these errors were encountered:
Hi @Sxjdwang,
I have tested more samples, but got bad effects too.
I am considering that might be because of the gap between the training dataset and my testing data, which is in-the-wild.
Would you mind give me some advice to reduce that gap?
Like face area resolution (although I think you will resize the cropped detected facial area)?
And the testing video fps == 25 and audio data sample rate == 16khz.
Dear Sir or Madam,
Thanks for making this projects open-sourced. Appreciate that.
But I found I cannot get a make-sense result. In most times, there are severe blur in the mouth area. Like the following video shows.
learn-english-00083.mp4
I am assuming that it is because the number of reference identity input is only one. It must be open-mouth or close mouth. So in one single generation period, the network cannot get both open-mouth and close-mouth identity characteristic feature of the face, so it will lead to much blur.
Please correct me if I was wrong.
The text was updated successfully, but these errors were encountered: