Training machine learning models to perform neural style transfer in three-dimensional shapes

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:

• applying one or more augmentations to a first input three-dimensional (3D) shape to generate a second input 3D shape;
• generating, via a first set of neural network layers, a style code based on a first latent representation of the first input 3D shape and a second latent representation of the second input 3D shape;
• generating, via a second set of neural network layers, a first output 3D shape based on the style code and the second latent representation;
• and
• performing one or more operations on the first set of neural network layers and the second set of neural network layers based on a first loss associated with the first output 3D shape to generate a trained machine learning model.

A system, comprising:

• one or more memories that store instructions, and
• one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of:
• applying one or more augmentations to a first input three-dimensional (3D) shape to generate a second input 3D shape;
• generating, via a first set of neural network layers, a style code based on a first latent representation of the first input 3D shape and a second latent representation of the second input 3D shape;
• generating, via a second set of neural network layers, a first output 3D shape based on the style code and the second latent representation;
• and
• performing one or more operations on the first set of neural network layers and the second set of neural network layers based on a first loss associated with the first output 3D shape to generate a trained machine learning model.