Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for generating insights for video games. The method includes gathering information regarding a player for a plurality of video games, the information comprising at least one of in-world state data, player action data, player progression data, and/or real-world events relevant to each video game. The method also includes tracking events in at least one video game of the plurality of video games, the events comprising an action event or a standby event. The method also includes determining that an event of the tracked events is an action event. The method also includes generating insights regarding the action event based on the information gathered regarding the player, the insights for improving the player's performance in the video game. The method also includes relaying the insights to the player to improve the player's performance in the video game.

Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.

Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.

Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.

Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.

Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.