Abstract: In one embodiment, a system may capture one or more images of a user using one or more cameras, the one or more images depicting at least an eye and a face of the user. The system may determine a direction of a gaze of the user based on the eye depicted in the one or more images. The system may generate a facial mesh based on depth measurements of one or more features of the face depicted in the one or more images. The system may generate an eyeball texture for an eyeball mesh by processing the direction of the gaze and the facial mesh using a machine-learning model. The system may render an avatar of the user based on the eyeball mesh, the eyeball texture, the facial mesh, and a facial texture.

Abstract: In one embodiment, a method includes accessing a number of pictures of an object, constructing a modeling volume for three-dimensional modeling of the object by processing the number of pictures using a machine-learning framework, where the modeling volume is associated with a number of color and opacity information that are associated with a number of regions in the modeling volume, and rendering an image of the object from a view-point using the modeling volume, where each pixel of the image is rendered by projecting a virtual ray from the view-point and through the modeling volume, determining one or more of the number of regions in the modeling volume intersected by the virtual ray, and determining a color and an opacity of the pixel based on an accumulation of the color and opacity information associated with the one or more of the number of regions intersected by the virtual ray.

Abstract: The disclosed computer system may include an input module, an autoencoder, and a rendering module. The input module may receive geometry information and images of a subject. The geometry information may be indicative of variation in geometry of the subject over time. Each image may be associated with a respective viewpoint and may include a view-dependent texture map of the subject. The autoencoder may jointly encode texture information and the geometry information to provide a latent vector. The autoencoder may infer, using the latent vector, an inferred geometry and an inferred view-dependent texture of the subject for a predicted viewpoint. The rendering module may be configured to render a reconstructed image of the subject for the predicted viewpoint using the inferred geometry and the inferred view-dependent texture. Various other systems and methods are also disclosed.

Abstract: The disclosed computer system may include an input module, an autoencoder, and a rendering module. The input module may receive geometry information and images of a subject. The geometry information may be indicative of variation in geometry of the subject over time. Each image may be associated with a respective viewpoint and may include a view-dependent texture map of the subject. The autoencoder may jointly encode texture information and the geometry information to provide a latent vector. The autoencoder may infer, using the latent vector, an inferred geometry and an inferred view-dependent texture of the subject for a predicted viewpoint. The rendering module may be configured to render a reconstructed image of the subject for the predicted viewpoint using the inferred geometry and the inferred view-dependent texture. Various other systems and methods are also disclosed.