Abstract: A virtual camera captures first images of a three-dimensional (3D) digital representation of a visual asset from different perspectives and under different lighting conditions. The first images are training images that are stored in a memory. One or more processors implement a generative adversarial network (GAN) that includes a generator and a discriminator, which are implemented as different neural networks. The generator generates second images that represent variations of the visual asset concurrently with the discriminator attempting to distinguish between the first and second images. The one or more processors update a first model in the discriminator and/or a second model in the generator based on whether the discriminator successfully distinguished between the first and second images. Once trained, the generator generates images of the visual asset based on the first model, e.g., based on a label or an outline of the visual asset.