Method for training face swap model, computer device, and storage medium

A computer device, comprising:

• one or more processors and a memory containing a computer-executable program that, when being executed, causes the one or more processors to perform:
• acquiring a sample triplet, the sample triplet comprising a source face image, a template image, and a reference image;
• concatenating an expression feature of the template image and an identity feature of the source face image to obtain a combined feature;
• performing encoding based on the source face image and the template image by using a generator network of a face swap model to obtain an encoding feature required for face swap;
• fusing the encoding feature and the combined feature to obtain a fused feature, comprising:
• calculating a mean value and a standard deviation of the encoding feature, and calculating a mean value and a standard deviation of the combined feature;
• normalizing the encoding feature based on the mean value and the standard deviation of the encoding feature to obtain a normalized encoding feature;
• and transferring a style of the combined feature to the normalized encoding feature based on the mean value and the standard deviation of the combined feature to obtain the fused feature;
• performing decoding based on the fused feature by using the generator network of the face swap model to obtain a swapped face image;
• respectively predicting an image attribute discrimination result of the swapped face image and an image attribute discrimination result of the reference image by using a discriminator network of the face swap model, an image attribute comprising forged image and non-forged image;
• and
• calculating a difference between an expression feature of the swapped face image and the expression feature of the template image, calculating a difference between an identity feature of the swapped face image and an identity feature of the source face image, and updating the generator network and the discriminator network, based on the image attribute discrimination result of the swapped face image and the image attribute discrimination result of the reference image, the calculated difference of the expression features between the swapped face image and the template image, and the calculated difference of the identity features between the swapped face image and the source face image.

A non-transitory computer-readable storage medium, storing a computer-executable instruction that, when being executed, causes one or more processors to perform:

• acquiring a sample triplet, the sample triplet comprising a source face image, a template image, and a reference image;
• concatenating an expression feature of the template image and an identity feature of the source face image to obtain a combined feature;
• performing encoding based on the source face image and the template image by using a generator network of the face swap model to obtain an encoding feature required for face swap;
• fusing the encoding feature and the combined feature to obtain a fused feature, comprising:
• calculating a mean value and a standard deviation of the encoding feature, and calculating a mean value and a standard deviation of the combined feature;
• normalizing the encoding feature based on the mean value and the standard deviation of the encoding feature to obtain a normalized encoding feature;
• and transferring a style of the combined feature to the normalized encoding feature based on the mean value and the standard deviation of the combined feature to obtain the fused feature;
• performing decoding based on the fused feature by using the generator network of the face swap model to obtain a swapped face image;
• respectively predicting an image attribute discrimination result of the swapped face image and an image attribute discrimination result of the reference image by using a discriminator network of the face swap model, an image attribute comprising forged image and non-forged image;
• and
• calculating a difference between an expression feature of the swapped face image and the expression feature of the template image, calculating a difference between an identity feature of the swapped face image and an identity feature of the source face image, and updating the generator network and the discriminator network, based on the image attribute discrimination result of the swapped face image and the image attribute discrimination result of the reference image, the calculated difference of the expression features between the swapped face image and the template image, and the calculated difference of the identity features between the swapped face image and the source face image.