Neural 3D-content-extension technique for AR environments.
Generating augmented reality content includes inputting a first layout of a physical space and a first set of anchor content into a machine learning model; generating, via execution of the machine learning model, a first augmented reality view that includes (i) a first portion of the physical space and (ii) an extension of the first set of anchor content across a second portion of the physical space; and causing the first augmented reality view to be outputted in a computing device.
BACKGROUND Field of the Various Embodiments (1) Embodiments of the present disclosure relate generally to machine learning and augmented reality and, more specifically, to neural extension of three-dimensional (3D) content in augmented reality environments. Description of the Related Art (2) Augmented reality (AR) refers to the merging of real-world and computer-generated content into an interactive sensory experience. For example, an AR system could include a camera, depth sensor, microphone, accelerometer, gyroscope, and/or another type of sensor that detects events or changes in the environment around a user. The AR system could also include a display, speaker, and/or another type of output device that combines data collected by the sensors with additional AR content into an immersive experience. The AR system could additionally modify the output of real-world and/or AR content in response to changes in the environment, interaction between the user and the AR content, and/or other input. (3) One application of AR involves combining traditional media content, such as images, audio, and/or video, with the layout of a real-world physical space. For example, an AR system executing on a wearable device or portable electronic device could “extend” an image or video of a scene across a room by overlaying objects, shapes, colors, and/or textures from the scene onto walls, ceilings, floors, and/or other parts of the room. The AR system could also arrange various portions of the sce
1. A computer-implemented method for generating augmented reality content, the method comprising: inputting a first layout of a physical space and a first set of anchor content into a machine learning model, wherein the first set of anchor content is represented within the physical space; generating, via execution of the machine learning model, a first three-dimensional (3D) volume that includes (i) a first subset of the physical space including the first set of anchor content and (ii) a placement of one or more 3D representations of the first set of anchor content in a second subset of the physical space, wherein: the placement of the one or more 3D representations is located at a different position within the physical space relative to a position of the first set of anchor content within the first subset of physical space, and generating the first 3D volume comprises: applying a first set of neural network layers included in the machine learning model to the first set of anchor content to generate a semantic segmentation of the first set of anchor content, and applying a second set of neural network layers included in the machine learning model to the first layout, the first set of anchor content, and the semantic segmentation to generate the first 3D volume; and causing one or more views of the first 3D volume to be outputted within an augmented reality environment provided by in a computing device. ||
10. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: inputting a first layout of a physical space and a first set of anchor content into a machine learning model, wherein the first set of anchor content is represented within the physical space; generating, via execution of the machine learning model, a first three-dimensional (3D) volume that includes (i) a first subset of the physical space including the first set of anchor content and (ii) a placement of one or more 3D representations of the first set of anchor content in a second subset of the physical space, wherein: the placement of the one or more 3D representations is located at a different position within the physical space relative to a position of the first set of anchor content within the first subset of physical space; and generating the first 3D volume comprises: applying a first set of neural network layers included in the machine learning model to the first set of anchor content to generate a semantic segmentation of the first set of anchor content, and applying a second set of neural network layers included in the machine learning model to the first layout, the first set of anchor content, and the semantic segmentation to generate the first 3D volume; and causing one or more views of the first 3D volume to be outputted within an augmented reality environment provided by in a computing device. ||
19. A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of: inputting a first layout of a physical space and a first set of anchor content into a machine learning model, wherein the first set of anchor content is represented within the physical space; generating, via execution of the machine learning model, a first three-dimensional (3D) volume that includes (i) a first subset of the physical space including the first set of anchor content and (ii) a placement of one or more 3D representations of the first set of anchor content in a second subset of the physical space, wherein: the placement of the one or more 3D representations is located at a different position within the physical space relative to a position of the first set of anchor content within the first subset of physical space; and generating the first 3D volume comprises: applying a first set of neural network layers included in the machine learning model to the first set of anchor content to generate a semantic segmentation of the first set of anchor content, and applying a second set of neural network layers included in the machine learning model to the first layout, the first set of anchor content, and the semantic segmentation to generate the first 3D volume; and causing one or more views of the first 3D volume to be outputted within an augmented reality environment provided by in a computing device.