Abstract: Systems and methods are provided for providing animated speech refinement. The systems and methods perform operations comprising: receiving an audio stream comprising one or more spoken words; processing the audio stream by an automated speech recognition (ASR) engine to identify base timing of one or more phonemes corresponding to the one or more spoken words; applying a machine learning model to the base of the one or more phonemes to estimate an adjustment to the base timing of the one or more phonemes.

Abstract: Systems and methods are provided for providing animated speech refinement. The systems and methods perform operations comprising: receiving an audio stream comprising one or more spoken words; processing the audio stream by an automated speech recognition (ASR) engine to identify base timing of one or more phonemes corresponding to the one or more spoken words; applying a machine learning model to the base of the one or more phonemes to estimate an adjustment to the base timing of the one or more phonemes.

Abstract: Characteristics of a user's movement are evaluated based on performance of activities by a user within a field of view of a camera. Video data representing performance of a series of movements by the user is acquired by the camera. Pose data is determined based on the video data, the pose data representing positions of the user's body while performing the movements. The pose data is compared to a set of existing videos that correspond to known errors to identify errors performed by the user. The errors may be used to generate scores for various characteristics of the user's movement. Based on the errors, exercises or other activities to improve the movement of the user may be determined and included in an output presented to the user.

Abstract: Video output is synchronized to the actions of a user by determining positions of the user's body based on acquired video of the user. The positions of the user's body are compared to the positions of a body shown in the video output to determine corresponding positions in the video output. The video output may then be synchronized so that the subsequent output that is shown corresponds to the subsequent position attempted by the user. The rate of movement of the user may be used to determine output characteristics for the video to cause the body shown in the video output to appear to move at a similar rate to that of the user. If the user moves at a rate less than a threshold or performs an activity erroneously, the video output may be slowed or portions of the video output may be repeated.

Abstract: First video data representing performance of an activity by a first user is acquired. Poses of the first user are determined from the first video data. Second video data is generated based on the determined poses and based on appearance data that represents a second user, such as a model, paid performer, and so forth, in various poses. The resulting second video data depicts the second user performing the same poses as the first user. The second video data may then be sent to a recipient. For example, a participant in an exercise class may send a video to an instructor that depicts what appears to be the paid performer performing the poses, instead of the participant. As a result, video data showing the participant is not shared, protecting the privacy of the participant while still allowing them to participate and interact.

Abstract: Systems and methods are provided for providing animated speech refinement. The systems and methods perform operations comprising: receiving an audio stream comprising one or more spoken words; processing the audio stream by an automated speech recognition (ASR) engine to identify base timing of one or more phonemes corresponding to the one or more spoken words; applying a machine learning model to the base of the one or more phonemes to estimate an adjustment to the base timing of the one or more phonemes.

Abstract: A first user generates video data for performance of an activity, such as a fitness exercise, by performing the activity in front of a camera. Based on the video data, the amount of movement of different parts of the first user's body is determined. Data representing the position of the first user over time is generated. The data may take the form of a function or a signal that is based on the function. The locations of body parts that move significantly are prioritized over other body parts when determining this data. At a subsequent time, a second user performs the activity. The number of times the second user completes the activity is counted by determining the number of times the second user reaches a position corresponding to a maximum value in the data representing the position of the first user.

Abstract: Characteristics of a user's movement are evaluated based on performance of activities by a user within a field of view of a camera. Video data representing performance of a series of movements by the user is acquired by the camera. Pose data is determined based on the video data, the pose data representing positions of the user's body while performing the movements. The pose data is compared to a set of existing videos that correspond to known errors to identify errors performed by the user. The errors may be used to generate scores for various characteristics of the user's movement. Based on the errors, exercises or other activities to improve the movement of the user may be determined and included in an output presented to the user.