Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.

Abstract: Systems and methods for animating an avatar are provided. An example method of animating an avatar includes at an electronic device having one or more processors and memory, receiving an audio input, receiving a video input including at least a portion of a user's face, wherein the video input is separate from the audio input, determining one or more movements of the user's face based on the received audio input and received video input, and generating, using a neural network separately trained with a set of audio training data and a set of video training data, a set of characteristics for controlling an avatar representing the one or more movements of the user's face.

Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.

Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.

Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.