Abstract: The present disclosure provides computer-implemented methods and systems to generate high dynamic range (HDR) panoramas to render virtual objects. A low dynamic range (LDR) panorama from a prompt describing a desired aspect of a panorama. The LDR panorama is then converted to an HDR panorama. The process can include generating a backplate image using a first machine learning model, projecting the image onto a sphere and inpainting it using a second machine learning model to generate an LDR panorama, and converting the LDR panorama to an HDR panorama using a third machine learning model. Each of the 10 first, second and third machine learning models can be diffusion models. Alternatively, the third machine learning model can be a convolutional neural network model. A physics-based rendering engine can then be used to render a virtual object in the HDR panorama.

Abstract: An automated and dynamic method and system are provided for estimating lighting conditions of a scene captured from a plurality of digital images. The method comprises generating 3D-source-specific-lighting parameters of the scene using a lighting-estimation neural network configured for: extracting from the plurality of images a corresponding number of latent feature vectors; transforming the latent feature vectors into common-coordinates latent feature vectors; merging the plurality of common-coordinates latent feature vectors into a single latent feature vector; and extracting, from the single latent feature vector, 3D-source-specific-lighting parameters of the scene.