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: In one embodiment, a method includes initializing latent codes respectively associated with times associated with frames in a training video of a scene captured by a camera. For each of the frames, a system (1) generates rendered pixel values for a set of pixels in the frame by querying NeRF using the latent code associated with the frame, a camera viewpoint associated with the frame, and ray directions associated with the set of pixels, and (2) updates the latent code associated with the frame and the NeRF based on comparisons between the rendered pixel values and original pixel values for the set of pixels. Once trained, the system renders output frames for an output video of the scene, wherein each output frame is rendered by querying the updated NeRF using one of the updated latent codes corresponding to a desired time associated with the output frame.

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 system configured to process an input point cloud, which represents an object using unstructured data points, to generate a feature vector that has an ordered structure and a fixed length. The system may process the input point cloud using a basis point set to generate the feature vector. For example, for each basis point in the basis point set, the system may identify a closest data point in the point cloud data and store a distance value or other information associated with the closest data point in the feature vector. The system may process the feature vector using a trained model to generate output data, such as performing point cloud registration to generate mesh data, point cloud classification to generate classification data, and/or the like.