Data augmentation using conditioned generative models for synthetic content generation

Abstract

Synthetic data generation systems and methods are disclosed for augmenting synthetic scenes using neural networks that are conditioned on depth information. The synthetic data generation system may use a guided latent diffusion model to generate (or augment) synthetic images, which can subsequently be used to train other models to perform tasks such as object detection. Input of the model may be an image rendered by a graphic engine with coarsely rendered objects. When the image is rendered, segmentation masks may also be generated for objects in the image. The synthetic data generation system may generate a monocular depth image. During the image generation phase, the corresponding segmentation mask, depth map, and any guiding textual input serve as constraints for the denoising process. During the denoising step, the synthetic data generation system may also crop and adjust the resolution of individual objects during diffusion to enhance the results. The newly regenerated object is then blended back into the original image to produce a synthetic image.

Dependencies are inferred from the claim text.