Electronic Arts Inc.

Electronics Arts, Inc.

Navid Aghdaie

Kazi Atif-Uz Zaman

Mohsen Sardari

John Kolen

Harold Henry Chaput

Kenneth Alan Moss

Mohamed Marwan Mattar

Su Xue

Kazi Zaman

Sundeep Narravula

Yiwei Zhao

Meng Wu

Igor Borovikov

Kilol Gupta

Zahra Shakeri

Han Liu

Wolfram Sebastian Starke

Reza Pourabolghasem

Dhaval Shah

Fernando De Mesentier Silva

Meredith Trotter

Jervis Pinto

Tushar Agarwal

Siddharth Gururani

Jesse Hans Stokes Harder

Nitish Victor

Yunqi Zhao

Daniel Lee Kading

John Christopher Epler

Thomas Murray Perlinski

Graham Francis Scott

Zhengxing Chen

Pawel Piotr Wrotek

Caedmon Somers

Jason Rupert

Jingwen Liang

Laura Greige

Ahmad Beirami

Preethi Ganeshan

Yu Jin

Qilian Yu

Wei Wang

Zebin Chen

Polina Igorevna Gouskova

Farah Mariam Ali

Jesse Harder

Chetan Nagaraja Rao

Mathieu Guindon

Mohamed Marwan Abdel Magid Mattar

Bhargav Rajendra

Chinmay Mukund Sumant

Cong Feng

Drew John Zagieboylo

Maziar Sanjabi

Mingyi Shi

Gordon Durity

Tushar Bansal

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.

Terrain generation and population system

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.

Neural animation layering for synthesizing movement

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.

Dynamic modifications of single player and multiplayer mode in a video game

A system for use in video game development to generate expressive speech audio comprises a user interface configured to receive user-input text data and a user selection of a speech style. The system includes a machine-learned synthesizer comprising a text encoder, a speech style encoder and a decoder. The machine-learned synthesizer is configured to generate one or more text encodings derived from the user-input text data, using the text encoder of the machine-learned synthesizer; generate a speech style encoding by processing a set of speech style features associated with the selected speech style using the speech style encoder of the machine-learned synthesizer; combine the one or more text encodings and the speech style encoding to generate one or more combined encodings; and decode the one or more combined encodings with the decoder of the machine-learned synthesizer to generate predicted acoustic features. The system includes one or more modules configured to process the predicted acoustic features, the one or more modules comprising a machine-learned vocoder configured to generate a waveform of the expressive speech audio.

Generating expressive speech audio from text data

This specification describes a computer-implemented method of generating context-dependent speech audio in a video game. The method comprises obtaining contextual information relating to a state of the video game. The contextual information is inputted into a prosody prediction module. The prosody prediction module comprises a trained machine learning model which is configured to generate predicted prosodic features based on the contextual information. Input data comprising the predicted prosodic features and speech content data associated with the state of the video game is inputted into a speech audio generation module. An encoded representation of the speech content data dependent on the predicted prosodic features is generated using one or more encoders of the speech audio generation module. Context-dependent speech audio is generated, based on the encoded representation, using a decoder of the speech audio generation module.

Speech prosody prediction in video games

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.

Live gameplay updates

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.

Automated pipeline selection for synthesis of audio assets

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.

Enhanced animation generation based on generative control

A collusion detection system may detect collusion between entities participating in online gaming. The collusion detection system may identify a plurality of entities associated with and opponents within an instance of an online game, determine social data associated with the plurality of entities, determine in-game behavior data associated with the plurality of entities, and determine, for one or more pairings of the plurality of entities, respective pairwise feature sets based at least in part on the social data and the in-game behavior data. The collusion detection system may then perform anomaly detection on the respective pairwise feature sets and, in response to the anomaly detection detecting one or more anomalous pairwise feature sets, output one or more suspect pairings of the plurality of entities corresponding to the one or more anomalous pairwise feature sets as suspected colluding pairings.

Detecting collusion in online games

A persona system determines a player persona for a player of a gaming system based on gameplay information for the user and, for example, performs dynamic content generation or additional product recommendations based on the player persona. The persona system may receive a request for content based on a persona of a player and receive gameplay data associated with gameplay of the player in a plurality of games. The persona system may then generate a player persona of the player based on the gameplay data associated with the gameplay of the player in the plurality of games, determine persona based content based at least in part on a portion of the player persona, and output the persona based content in response to the request.

Persona driven dynamic content framework

Some embodiments herein disclose intelligent priority evaluators configured to perform a method that prioritizes tasks submitted by various users, even if the tasks are similarly classified. The scheduling system can collect, calculate, and use various criteria to determine a reward score in order to prioritize one task over another, such as for dynamic scheduling purposes. This can be performed in addition to or as a replacement for receiving user designations of priority.

Intelligent system for automated allocation of computing resources

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.

Enhanced animation generation based on motion matching using local bone phases

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San Jose, CA, United States of America

Navid Aghdaie