AI has already shown off the capability to create photorealistic images of cats, dogs, and people’s faces that never existed before. More recently, researchers have been investigating how to train AI models to create more complex images that could include many different objects arranged in different poses and configurations.

The challenge involves figuring out how to get AI models—in this case typically a class of deep learning algorithms known as generative adversarial networks (GANs)—to generate more controlled images based on certain conditions rather than simply spitting out any random image. A team at North Carolina State University has developed a way for GANs to create such conditional images more reliably by using reconfigurable image layouts as the starting point.

“We want a model that is flexible enough such that when the input layout is reconfigurable, then we can generate an image that can be consistent,” says Tianfu Wu, an assistant professor in the department of electrical and computer engineering at North Carolina State University in Raleigh.