Abstract: An electronic device may include a light based image generation system configured to generate images of a 3-dimensional object in a scene. The light based image generation system can include a feature extractor, a triplane decoder, and a volume renderer. The feature extractor can receive lighting information about the scene and a perspective of the object in the scene and generate corresponding triplane features. The triplane decoder can decode diffuse and specular reflection parameters based on the triplane features. The volume renderer can render a set of images based on the diffuse and specular reflection parameters. A super resolution image can be generated from the set of images and compared with ground truth images to fine tune weights, biases, and other machine learning parameters associated with the light based image generation system. The light based image generation system can be conditioned to generate photorealistic images of human faces.

Abstract: Systems and methods for a secure application computing environment in a federated Edge Cloud are disclosed herein. Application information corresponding to an application from an application provider may be received in a secure environment of a device such as an edge computing node. A trust level for execution of the application may be associated based at least in part on the application information, and based on the associated trust level, a trusted platform may be selected to deploy or launch the application.

Abstract: In one embodiment, a method includes a computing system accessing a first training data comprising a first image and a second image and an associated optical flow estimation. The system may input (1) the first image into a first machine-learning model configured to generate a first output and (2) the optical flow estimation into a second machine-learning model configured to generate a second output. The first output of the first machine-learning model is associated with first image segments of a predetermined number, and the second output of the second machine-learning model is associated with transformations of the predetermined number. The first output, the transformations, and the first image are configured to generate an estimated image. The system trains the first machine-learning model and the second machine-learning model based on at least a comparison of the estimated image and the second image.

Abstract: In one embodiment, a method includes a computing system accessing a first training data comprising a first image and a second image and an associated optical flow estimation. The system may input (1) the first image into a first machine-learning model configured to generate a first output and (2) the optical flow estimation into a second machine-learning model configured to generate a second output. The first output of the first machine-learning model is associated with first image segments of a predetermined number, and the second output of the second machine-learning model is associated with transformations of the predetermined number. The first output, the transformations, and the first image are configured to generate an estimated image. The system trains the first machine-learning model and the second machine-learning model based on at least a comparison of the estimated image and the second image.