Cross-domain image translation

1. A computer-implemented method, comprising:

determining a first learning network for geometric deformation from a first image domain to a second image domain based on a first image in the first image domain and a second image in the second image domain, images in the first and second image domains having different styles and objects in the images having geometric deformation with respect to each other, wherein the first learning network is based on a first generative adversarial network (GAN), and wherein determining the first learning network comprises:

performing, by using a first sub-network of the first GAN, geometric deformation from the first image domain to the second image domain on the first image, to deform a first geometry of an object in the first image to a first deformed geometry;

determining a first difference between the first geometry and a first average geometry and a second difference between the first deformed geometry and a second average geometry, the first average geometry being an average of geometries of objects in a plurality of images in the first image domain, and the second average geometry being an average of geometries of objects in a plurality of images in the second image domain; and

updating the first sub-network of the first GAN such that a difference between the first and second differences is decreased, wherein the updated first sub-network is determined as the first learning network;

performing the geometric deformation from the second image domain to the first image domain on the second image to generate an intermediate image, the intermediate image inheriting a same style as the second image; and

determining a second learning network for style transfer from the first image domain to the second image domain based on the first image and the intermediate image.

2. The method of claim 1 , wherein determining the first learning network comprises:

extracting first landmark points of a geometry of an object in the first image and second landmark points of a geometry of an object in the second image;

determining a first principal component analysis (PCA) representation of the first landmark points and a second PCA representation of the second landmark points; and

determining the first learning network based on the first and second PCA representations.

3. The method of claim 1 , wherein updating the first sub-network of the first GAN further comprises:

performing, by using a second sub-network of the first GAN, the geometric deformation from the second image domain to the first image domain on the second image, to deform a second geometry of an object in the second image to a second deformed geometry;

determining a third difference between the second geometry and the second average geometry and a fourth difference between the second deformed geometry and the first average geometry; and

jointly updating the first and second sub-networks of the first GAN such that a difference between the third and fourth differences is also decreased.

4. The method of claim 3 , wherein performing the geometric deformation from the second image domain to the first image domain comprises:

performing the geometric deformation from the second image domain to the first image domain by using the updated second sub-network.

5. The method of claim 1 , wherein the second learning network is based on a second generative adversarial network (GAN), and wherein determining the second learning network comprises:

performing, by using a first sub-network of the second GAN, style transfer from the first image domain to the second image domain on the first image, to transfer the first image with a first style to a first predicted image with a second style, the second style being randomly selected from a plurality of styles in the second image domain; and

updating the first sub-network of the second GAN such that a difference in content between the first predicted image and the first image is decreased,

wherein the updated first sub-network is determined as the second learning network.

6. The method of claim 5 , wherein updating the first sub-network of the second GAN further comprises:

performing, by using a second sub-network of the second GAN, style transfer from the second image domain to the first image domain on the intermediate image, to transfer the intermediate image with a third style to a second predicted image with a fourth style, the fourth style being randomly selected from a plurality of styles in the first image domain; and

jointly updating the first and second sub-networks of the second GAN such that a difference in content between the second predicted image and the intermediate image is decreased.

7. A computer-implemented method, comprising:

performing geometric deformation on a source image to deform a first geometry of an object in the source image to a second geometry;

performing style transfer on the source image to transfer the source image with a first style to an intermediate image with a second style; and

generating a target image based on the second geometry of the source image and the second style of the intermediate image by performing image warping on the intermediate image under the guidance of the deformed second geometry such that an object in the target image has the second geometry while the style of the target image remains consistent with the second style of the intermediate image.

8. The method of claim 7 , wherein performing the geometric deformation comprises:

determining landmark points in the source image that represent the first geometry;

generating a principal component analysis (PCA) representation of the landmark points;

performing the geometric deformation on the PCA representation; and

determining deformed landmark points representing the second geometry based on the deformed PCA representation.

9. The method of claim 7 , wherein performing the geometric deformation comprises:

obtaining an indication of a deformation degree of the object; and

transferring the first geometry to the second geometry based on the deformation degree.

10. The method of claim 7 , wherein performing style transfer on the source image comprises:

extracting a content-related feature representation of the source image;

determining an appearance-related feature representation associated with the second style; and

generating the intermediate image based on the content-related feature representation and the appearance-related feature representation.

11. The method of claim 10 , wherein determining the appearance-related feature representation comprises:

randomly selecting the appearance-related feature representation from a plurality of predetermined appearance-related feature representations; or

determining the appearance-related feature representation based on an obtained indication related to the second style or a reference image with the second style.

12. The method of claim 7 , wherein performing the geometric deformation comprises performing the geometric deformation by using a first learning network, the first learning network being determined based on a first generative adversarial network (GAN); or

wherein performing the style transfer comprises performing the style transfer by using a second learning network, the second learning network being determined based on a second GAN network.