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 system and method for providing live gameplay updates receives a modification for a videogame, the modification affecting a gameplay aspect of the videogame during execution of the videogame. The system and method determine a target group for deploying the modification. The target group includes first one or more live instances of the videogame. The system and method deploy the modification to the target group and receive gameplay data associated with the gameplay aspect from the target group. The system and method deploy the modification to second one or more live instances of the videogame based at least in part on an analysis of the received gameplay data.

Abstract: The present disclosure provides embodiments for joint twist generation for animation. The system can utilize a neural network, also referred to as a deep neural network, which utilizes machine learning processes in order to create animation data that are more life-like and realistic. The system can obtain a set of axis vectors for a rig of a virtual character model; obtain a twist model for the rig; input the set of axis vectors to the twist model to obtain a set of twist vectors; and determine animation data based on the set of axis vectors and the set of twist vectors.

Abstract: An example method of automated selection of audio asset synthesizing pipelines includes: receiving an audio stream comprising human speech; determining one or more features of the audio stream; selecting, based on the one or more features of the audio stream, an audio asset synthesizing pipeline; training, using the audio stream, one or more audio asset synthesizing models implementing respective stages of the selected audio asset synthesizing pipeline; and responsive to determining that a quality metric of the audio asset synthesizing pipeline satisfies a predetermined quality condition, synthesizing one or more audio assets by the selected audio asset synthesizing pipeline.

Abstract: Systems and methods are provided for enhanced animation generation based on generative control models. An example method includes accessing an autoencoder trained based on character control information generated using motion capture data, the character control information indicating, at least, trajectory information associated with the motion capture data, and the autoencoder being trained to reconstruct, via a latent feature space, the character control information. First character control information associated with a trajectory of an in-game character of an electronic game is obtained. A latent feature representation is generated and the latent feature representation is modified. A control signal is output to a motion prediction network for use in updating a character pose of the in-game character.

Abstract: A video game system and method analyze virtual contact between an avatar and a virtual object within a video game. The point of contact of the virtual contact on the virtual object and/or the intensity of contact of the virtual contact may then be used to determine a subsequent virtual action to be performed within the video game. The virtual action, with any virtual movement thereof, may be carried out in a realistic manner within the video game by determining a virtual trajectory of the motion. The virtual trajectory may be determined using a motion model. The motion model may provide the virtual trajectory of the virtual object based at least in part on one or more parameters of the virtual object, such as a weight parameter. The motion model may be trained using training video clips with realistic motion of virtual objects.

Abstract: A personalization system determines a playstyle associated with a player of a gaming system based on gameplay information for the player and, for example, generates personalized animation for the player based on the player's playstyle. The personalization system can receive gameplay data associated with a playstyle of a player in one or more games and receive persona data associated with the player and the gameplay. The persona system can generate an animation for the player based on the gameplay data associated with the playstyle of the player in the one or more games, dynamically generate, based at least in part on a portion of the playstyle of the player, content including personalized animation, wherein the content including personalized animation is dynamically generated personalized content associated with the player, and transmit the content including personalized animation for presentation in a game associated with the player.

Abstract: A system may perform animation retargeting that may allow an existing animation to be repurposed for a different skeleton and/or a different environment geometry from that associated with the existing animation. The system may input, to a machine learning (ML) retargeting model, an input animation, a target skeleton and environment geometry data of an environment for a predicted animation, wherein the ML retargeting model is configured to generate the predicted animation based on the input animation, the target skeleton and the environment geometry data of the environment for the predicted animation and receive, from the ML retargeting model, the predicted animation based on the input animation, the target skeleton and the environment geometry data of the environment for the predicted animation.

Abstract: A device may access a feature vector generated based on interactions by a user with a video game. The device may access a cluster map comprising a mapping of user clusters, wherein each location within the cluster map is associated with a set of users whose feature vectors are within a threshold degree of similarity of each other. The cluster map may be generated using a plurality of extracted feature vectors obtained from interaction information. A device may determine a map location within the cluster map associated with the user based at least in part on the feature vector. A device may determine a target map location within the cluster map. A device may determine a guidance action based at least in part on the target map location and the map location associated with the user. A device may execute the guidance action.

Abstract: Systems and methods are provided for enhanced face shape generation for virtual entities based on generative modeling techniques. An example method includes training models based on synthetically generated faces and information associated with an authoring system. The modeling system being trained to reconstruct face shapes for virtual entities based on a latent space embedding of a face identity.

Abstract: A computing system may provide functionality for controlling an animated model to perform actions and to perform transitions therebetween. The system may determine, from among a plurality of edges from a first node of a control graph to respective other nodes of the control graph, a selected edge from the first control node to a selected node. The system may then determine controls for an animated model in a simulation based at least in part on the selected edge, control data associated with the selected node, a current simulation state of the simulation, and a machine learned algorithm, determine an updated simulation state of the simulation based at least in part on the controls for the animated model, and adapt one or more parameters of the machine learned algorithm based at least in part on the updated simulation state and a desired simulation state.

Abstract: A video game includes a single player mode where completion of storyline objectives advances the single player storyline. The video game also includes a multiplayer mode where a plurality of players can play on an instance of a multiplayer map. Storyline objectives from the single player mode are selected and made available for completion to players in the multiplayer mode, and the single player storylines can be advanced by players completing respective storyline objectives while playing in the multiplayer mode. Combinations of storyline objectives are selected from pending storyline objectives for players connecting to a multiplayer game for compatibility with multiplayer maps. Constraints can be used to determine compatibility.

Abstract: In some embodiments, the dynamic animation generation system can provide a deep learning framework to produce a large variety of martial arts movements in a controllable manner from unstructured motion capture data. The system can imitate animation layering using neural networks with the aim to overcome challenges when mixing, blending and editing movements from unaligned motion sources. The system can synthesize movements from given reference motions and simple user controls, and generate unseen sequences of locomotion, but also reconstruct signature motions of different fighters. For achieving this task, the dynamic animation generation system can adopt a modular framework that is composed of the motion generator, that maps the trajectories of a number of key joints and root trajectory to the full body motion, and a set of different control modules that map the user inputs to such trajectories.

Abstract: Embodiments of the systems and methods described herein provide a terrain generation and population system that can determine terrain population rules for terrain population objects and features when placing objects and features in a three dimensional virtual space. As such, the terrain generation and population system can generate realistic terrain for use in game. The terrain generation and population system can receive an image, such as a satellite image, and utilize artificial intelligence to perform image segmentation at the pixel level to segment features and/or objects in the image. The game terrain system can automatically detect and apply feature and object masks based on the identified features and/or objects from the image segmentation. The game terrain system can place the features and/or objects in corresponding masks in the three dimensional space according to the application of terrain population rules.

Abstract: A computing system may provide functionality for controlling an animated model to perform actions and to perform transitions therebetween. The system may determine, from among a plurality of edges from a first node of a control graph to respective other nodes of the control graph, a selected edge from the first control node to a selected node. The system may then determine controls for an animated model in a simulation based at least in part on the selected edge, control data associated with the selected node, a current simulation state of the simulation, and a machine learned algorithm, determine an updated simulation state of the simulation based at least in part on the controls for the animated model, and adapt one or more parameters of the machine learned algorithm based at least in part on the updated simulation state and a desired simulation state.

Abstract: Systems and methods are provided for enhanced animation generation based on using motion mapping with local bone phases. An example method includes accessing first animation control information generated for a first frame of an electronic game including local bone phases representing phase information associated with contacts of a plurality of rigid bodies of an in-game character with an in-game environment. Executing a local motion matching process for each of the plurality of local bone phases and generating a second pose of the character model based on the plurality of matched local poses for a second frame of the electronic game.

Abstract: System and methods for using a deep learning framework to customize animation of an in-game character of a video game. The system can be preconfigured with animation rule sets corresponding to various animations. Each animation can be comprised of a series of distinct poses that collectively form the particular animation. The system can provide an animation-editing interface that enables a user of the video game to make modifications to at least one pose or frame of the animation. The system can realistically extrapolate these modifications across some or all portions of the animation. In addition or alternatively, the system can realistically extrapolate the modifications across other types of animations.

Abstract: A video game includes a single player mode where completion of storyline objectives advances the single player storyline. The video game also includes a multiplayer mode where a plurality of players can play on an instance of a multiplayer map. Storyline objectives from the single player mode are selected and made available for completion to players in the multiplayer mode, and the single player storylines can be advanced by players completing respective storyline objectives while playing in the multiplayer mode. Combinations of storyline objectives are selected from pending storyline objectives for players connecting to a multiplayer game for compatibility with multiplayer maps. Constraints can be used to determine compatibility.

Abstract: Embodiments of the systems and methods described herein provide a terrain generation and population system that can determine terrain population rules for terrain population objects and features when placing objects and features in a three dimensional virtual space. As such, the terrain generation and population system can generate realistic terrain for use in game. The terrain generation and population system can receive an image, such as a satellite image, and utilize artificial intelligence to perform image segmentation at the pixel level to segment features and/or objects in the image. The game terrain system can automatically detect and apply feature and object masks based on the identified features and/or objects from the image segmentation. The game terrain system can place the features and/or objects in corresponding masks in the three dimensional space according to the application of terrain population rules.

Abstract: A system and method for providing live gameplay updates receives a modification for a videogame, the modification affecting a gameplay aspect of the videogame during execution of the videogame. The system and method determine a target group for deploying the modification. The target group includes first one or more live instances of the videogame. The system and method deploy the modification to the target group and receive gameplay data associated with the gameplay aspect from the target group. The system and method deploy the modification to second one or more live instances of the videogame based at least in part on an analysis of the received gameplay data.