Abstract: A method includes receiving a first wireless signal detected by a first device in an environment, the first wireless signal including a first distortion pattern caused by an object moving in the environment, receiving a second wireless signal detected by a second device in the environment, the second wireless signal including a second distortion pattern caused by the object moving in the environment, determining, by comparing the first distortion pattern to the second distortion pattern, that the first distortion pattern and the second distortion pattern correspond to a same movement event associated with the object moving in the environment, determining a timing offset between the first device and the second device based on information associated with the first distortion pattern and the second distortion pattern, and determining, based on the timing offset, temporal correspondences between data generated by the first device and data generated by the second device.

Abstract: In one embodiment, a method includes adjusting parameters of a three-dimensional geometry corresponding to a first person to make the three-dimensional geometry represent a desired pose for the first person, accessing a neural texture encoding an appearance of the first person, generating a first rendered neural texture based on a mapping between (1) a portion of the three-dimensional geometry that is visible from a viewing direction and (2) the neural texture, generating a second rendered neural texture by processing the first rendered neural texture using a first neural network, determining normal information associated with the portion of the three-dimensional geometry that is visible from the viewing direction, and generating a rendered image for the first person in the desired pose by processing the second rendered neural texture and the normal information using a second neural network.

Abstract: A method includes receiving a first wireless signal detected by a first device in an environment, the first wireless signal including a first distortion pattern caused by an object moving in the environment, receiving a second wireless signal detected by a second device in the environment, the second wireless signal including a second distortion pattern caused by the object moving in the environment, determining, by comparing the first distortion pattern to the second distortion pattern, that the first distortion pattern and the second distortion pattern correspond to a same movement event associated with the object moving in the environment, determining a timing offset between the first device and the second device based on information associated with the first distortion pattern and the second distortion pattern, and determining, based on the timing offset, temporal correspondences between data generated by the first device and data generated by the second device.

Abstract: In one embodiment, a method includes adjusting parameters of a three-dimensional geometry corresponding to a first person to make the three-dimensional geometry represent a desired pose for the first person, accessing a neural texture encoding an appearance of the first person, generating a first rendered neural texture based on a mapping between (1) a portion of the three-dimensional geometry that is visible from a viewing direction and (2) the neural texture, generating a second rendered neural texture by processing the first rendered neural texture using a first neural network, determining normal information associated with the portion of the three-dimensional geometry that is visible from the viewing direction, and generating a rendered image for the first person in the desired pose by processing the second rendered neural texture and the normal information using a second neural network.

Abstract: Examples of the present disclosure describe systems and methods for scene reconstruction from bursts of image data. In an example, an image capture device may gather information from multiple positions within the scene. At each position, a burst of image data may be captured, such that other images within the burst may be used to identify common image features, anchor points, and geometry, in order to generate a scene reconstruction as observed from the position. Thus, as a result of capturing bursts from multiple positions in a scene, multiple burst reconstructions may be generated. Each burst may be oriented within the scene by identifying a key frame for each burst and using common image features and anchor points among the key frames to determine a camera position for each key frame. The burst reconstructions may then be combined into a unified reconstruction, thereby generating a high-quality reconstruction of the scene.

Abstract: Examples of the present disclosure describe systems and methods for scene reconstruction from bursts of image data. In an example, an image capture device may gather information from multiple positions within the scene. At each position, a burst of image data may be captured, such that other images within the burst may be used to identify common image features, anchor points, and geometry, in order to generate a scene reconstruction as observed from the position. Thus, as a result of capturing bursts from multiple positions in a scene, multiple burst reconstructions may be generated. Each burst may be oriented within the scene by identifying a key frame for each burst and using common image features and anchor points among the key frames to determine a camera position for each key frame. The burst reconstructions may then be combined into a unified reconstruction, thereby generating a high-quality reconstruction of the scene.