Abstract: Training data from multiple types of sensors and captured in previous capture sessions can be fused within a physics-based tracking framework to train motion priors using different deep learning techniques, such as convolutional neural networks (CNN) and Recurrent Temporal Restricted Boltzmann Machines (RTRBMs). In embodiments employing one or more CNNs, two streams of filters can be used. In those embodiments, one stream of the filters can be used to learn the temporal information and the other stream of the filters can be used to learn spatial information. In embodiments employing one or more RTRBMs, all visible nodes of the RTRBMs can be clamped with values obtained from the training data or data synthesized from the training data. In cases where sensor data is unavailable, the input nodes may be unclamped and the one or more RTRBMs can generate the missing sensor data.

Abstract: Individual images for individual frames of an animation may be rendered to include individual focal areas. A focal area may include one or more of a foveal region corresponding to a gaze direction of a user, an area surrounding the foveal region, and/or other components. The foveal region may comprise a region along the user's line of sight that permits high visual acuity with respect to a periphery of the line of sight. A focal area within an image may be rendered based on parameter values of rendering parameters that are different from parameter values for an area outside the focal area.

Abstract: Training data from multiple types of sensors and captured in previous capture sessions can be fused within a physics-based tracking framework to train motion priors using different deep learning techniques, such as convolutional neural networks (CNN) and Recurrent Temporal Restricted Boltzmann Machines (RTRBMs). In embodiments employing one or more CNNs, two streams of filters can be used. In those embodiments, one stream of the filters can be used to learn the temporal information and the other stream of the filters can be used to learn spatial information. In embodiments employing one or more RTRBMs, all visible nodes of the RTRBMs can be clamped with values obtained from the training data or data synthesized from the training data. In cases where sensor data is unavailable, the input nodes may be unclamped and the one or more RTRBMs can generate the missing sensor data.

Abstract: Individual images for individual frames of an animation may be rendered to include individual focal areas. A focal area may include one or more of a foveal region corresponding to a gaze direction of a user, an area surrounding the foveal region, and/or other components. The foveal region may comprise a region along the user's line of sight that permits high visual acuity with respect to a periphery of the line of sight. A focal area within an image may be rendered based on parameter values of rendering parameters that are different from parameter values for an area outside the focal area.