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: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

Abstract: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

Abstract: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

Abstract: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

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.