Three-dimensional rotation of two-dimensional vector graphics utilizing diffusion models

A system comprising:

• a memory component;
• and
• one or more processing devices coupled to the memory component, the one or more processing devices to perform operations comprising:
• receiving a user input to rotate an object depicted in a two-dimensional vector graphic from a first orientation through a three-dimensional space into a second orientation;
• concatenating, in a height dimension, a rasterized image of the two-dimensional vector graphic with a noised image to generate a vertically concatenated input image;
• generating, from the vertically concatenated input image utilizing a diffusion neural network, a new image comprising a denoised image depicting the object in the second orientation according to the user input;
• and
• cropping the denoised image depicting the object in the second orientation from the new image.

A non-transitory computer-readable medium storing executable instructions that, when executed by a processing device, cause the processing device to perform operations comprising:

• accessing a first albedo-only view of a three-dimensional shape in a first orientation and a second albedo-only view of the three-dimensional shape in a second orientation;
• generating, utilizing a diffusion neural network, a two-dimensional graphic depicting the three-dimensional shape rotated into the second orientation from the first albedo-only view;
• and
• adjusting parameters of the diffusion neural network to reduce a measure of loss determined by comparing the two-dimensional graphic and the second albedo-only view.