Abstract: A method includes receiving a first image including color data and depth data, determining a viewpoint associated with an augmented reality (AR) and/or virtual reality (VR) display displaying a second image, receiving at least one calibration image including an object in the first image, the object being in a different pose as compared to a pose of the object in the first image, and generating the second image based on the first image, the viewpoint and the at least one calibration image.

Abstract: Three-dimensional (3D) performance capture and machine learning can be used to re-render high quality novel viewpoints of a captured scene. A textured 3D reconstruction is first rendered to a novel viewpoint. Due to imperfections in geometry and low-resolution texture, the 2D rendered image contains artifacts and is low quality. Accordingly, a deep learning technique is disclosed that takes these images as input and generates more visually enhanced re-rendering. The system is specifically designed for VR and AR headsets, and accounts for consistency between two stereo views.

Abstract: A method includes receiving a first image including color data and depth data, determining a viewpoint associated with an augmented reality (AR) and/or virtual reality (VR) display displaying a second image, receiving at least one calibration image including an object in the first image, the object being in a different pose as compared to a pose of the object in the first image, and generating the second image based on the first image, the viewpoint and the at least one calibration image.

Abstract: In at least one aspect, a method can include generating a respective set of training set of images for each label in a handedness model by: receiving the label at an image capturing device, obtaining a set of captured images by recording a pass-through image of a user placing a target object within an overlay of a bounding area animation, the target object corresponding with the label, and associating the label with each image in the set of captured images. The method includes training, using the training images, the handedness model to provide a correct label for an input image.

Abstract: In at least one aspect, a method can include generating a respective set of training set of images for each label in a handedness model by: receiving the label at an image capturing device, obtaining a set of captured images by recording a pass-through image of a user placing a target object within an overlay of a bounding area animation, the target object corresponding with the label, and associating the label with each image in the set of captured images. The method includes training, using the training images, the handedness model to provide a correct label for an input image.