Providing user guidance to use and train a generative adversarial network

A system for providing user guidance to use and train a generative adversarial network, comprising:

• a discriminator;
• a generator;
• a computer readable storage medium having computer readable program code embodied therein that is executable to perform operations, the operations comprising:
• training the discriminator, implementing machine learning, to output a high probability from input comprising an input image comprising a real image representing a desirable design item superimposed on a body in a pose represented in the input image, metadata of a context of the input image, and metadata of the pose of the body represented in the input image, wherein the metadata of the pose indicates positions of body keypoints that represent the pose on which the desirable design item will be superimposed, wherein the desirable design item comprises a design item desirable to a target audience;
• executing the generator, implementing machine learning, to generate an output image based on an input context of a design item to generate, an input pose of a body on which the design item will be superimposed in the output image, and random noise;
• outputting, by the discriminator, a probability the output image, from the generator, represents a real image having a desirable design item from input comprising the output image, the input context, and the input pose;
• and
• training the generator to output the output image for the input pose and the input context with the probability outputted by the discriminator.

A method for providing user guidance to use and train a generative adversarial network, comprising:

• training a discriminator, implementing machine learning, to output a high probability from input comprising an input image comprising a real image representing a desirable design item superimposed on a body in a pose represented in the input image, metadata of a context of the input image, and metadata of the pose of the body represented in the input image, wherein the metadata of the pose indicates positions of body keypoints that represent the pose on which the desirable design item will be superimposed, wherein the desirable design item comprises a design item desirable to a target audience;
• executing a generator, implementing machine learning, to generate an output image based on an input context of a design item to generate, an input pose of a body on which the design item will be superimposed in the output image, and random noise;
• outputting, by the discriminator, a probability the output image, from the generator, represents a real image having a desirable design item from input comprising the output image, the input context, and the input pose;
• and
• training the generator to output the output image for the input pose and the input context with the probability outputted by the discriminator.