Electronic Arts Inc.

Block, Inc.

Electronics Arts, Inc.

Mohsen Sardari

Navid Aghdaie

Kazi Atif-Uz Zaman

John Kolen

Harold Henry Chaput

Mohamed Marwan Mattar

Su Xue

Kenneth Alan Moss

Igor Borovikov

Kazi Zaman

Yiwei Zhao

Zahra Shakeri

Kilol Gupta

Han Liu

Wolfram Sebastian Starke

Meng Wu

Dhaval Shah

Siddharth Gururani

Jingwen Liang

Jervis Pinto

Pawel Piotr Wrotek

Caedmon Somers

Jason Rupert

Tushar Agarwal

Yunqi Zhao

Ankit Dutta

Yibo Chen

Nic Kleene

Yi Li

Pooja Choudhary

Kin Ho Lee

Ahmad Beirami

Jesse Harder

Nitish Victor

Ping Zhong

Polina Igorevna Gouskova

Farah Mariam Ali

Mingyi Shi

Maziar Sanjabi

Schaefer Mitchell

Gary Ng Thow Hing

Khilesh Mistry

Ran Lin

Jonathan Lamberts

Anna Bloom

Sagnik Mazumder

Chetan Nagaraja Rao

Gordon Durity

Mathieu Guindon

Mohamed Marwan Abdel Magid Mattar

Bhargav Rajendra

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 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 payment service system receives contextual information regarding an interaction between the payment service and a user device associated with a user. A propensity metric for the user is determined based at least in part on inputting the contextual information into a machine learning (ML) model. Based on the propensity metric, a user interface is dynamically configured to comprise user interface elements arranged in a layout personalized for the user, where a user interface element represents content particular to a service offered by the payment service. Based on receiving an interaction with the user interface element, a booklet is launched corresponding to the service with which the user interface element is associated.

Model-based personalization architecture

This specification describes a computer-implemented generative interior design method. The method comprises obtaining input data comprising boundary data. The boundary data defines a boundary of an interior region of a video game building. A floor plan for the interior region of the video game building is generated. This comprises processing the input data using a floor plan generator model. The floor plan divides the interior region into a plurality of interior spaces. A layout for at least one of the plurality of interior spaces defined by the floor plan is generated by a layout generator model comprising one or more graph neural networks. The layout represents a configuration of one or more objects to be placed in the interior region.

Generative interior design in video games

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

This specification describes a computing system for generating visual assets for video games. The computing system comprises an image segmentation model, a first 3D generation model, and a second 3D generation model. At least one of the first 3D generation model and the second 3D generation model comprises a machine-learning model. The system is configured to obtain: (i) a plurality of images corresponding to the visual asset, each image showing a different view of an object to be generated in the visual asset, and (ii) orientation data for each image that specifies an orientation of the object in the image. A segmented image is generated for each image. This comprises processing the image using the image segmentation model to segment distinct portions of the image into one or more classes of a predefined set of classes. For each image, 3D shape data is generated for a portion of the object displayed in the image. This comprises processing the segmented image of the image, the orientation data of the image, and style data for the visual asset using the first 3D generation model. 3D shape data is generated for the visual asset. This comprises processing the generated 3D shape data of each image using the second 3D generation model.

Style-controlled generation of visual assets for video games using machine learning

Using transaction data to present search results is described. A payment service computing platform may receive transaction data associated with users of a payment service, and may receive a search query from a payment application associated with the payment service and executing on a device of a user. The payment service computing platform may then determine, based at least in part on a portion of the transaction data associated with the user, a user risk metric associated with the user, may generate a list of entities based at least in part on the user risk metric, and may cause a user interface of the payment application to present at least a portion of the list of entities as a search result to the search query.

Using transaction data to present search results

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

Neural animation layering for synthesizing martial arts movements

Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Fraud detection system

Growing community of inventors

Redwood City, CA, United States of America

Mohsen Sardari