Abstract: A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy.

Abstract: One aspect of the disclosure is a non-transitory computer-readable storage medium including program instructions. Operations performed by execution of the program instructions include obtaining an input image that depicts a face of a subject, having an initial facial expression and an initial pose, determining a reference shape description based on the input image, determining a target shape description based on the reference shape description, a facial expression difference, and a pose difference, generating a rendered target shape image using the target shape description, and generating an output image based on the input image and the rendered target shape using an image generator, wherein the output image is a simulated image of the subject of the input image that has a final expression that is based on the initial facial expression and the facial expression difference, and a final pose that is based on the initial pose and the pose difference.

Abstract: One aspect of the disclosure is a non-transitory computer-readable storage medium including program instructions. Operations performed by execution of the program instructions include obtaining an input image that depicts a face of a subject, having an initial facial expression and an initial pose, determining a reference shape description based on the input image, determining a target shape description based on the reference shape description, a facial expression difference, and a pose difference, generating a rendered target shape image using the target shape description, and generating an output image based on the input image and the rendered target shape using an image generator, wherein the output image is a simulated image of the subject of the input image that has a final expression that is based on the initial facial expression and the facial expression difference, and a final pose that is based on the initial pose and the pose difference.

Abstract: A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy.