Electronic Arts Inc.

Electronics Arts, Inc.

Han Liu

Mohsen Sardari

Navid Aghdaie

Harold Henry Chaput

Kazi Atif-Uz Zaman

Jingwen Liang

Kenneth Alan Moss

Yiwei Zhao

Kazi Zaman

Jesse Harder

Mathieu Guindon

Schaefer Mitchell

Gary Ng Thow Hing

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

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

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

This specification describes a system for generating positions of map items such as buildings, for placement on a virtual map. The system comprises: at least one processor; and a non-transitory computer-readable medium including executable instructions that when executed by the at least one processor cause the at least one processor to perform at least the following operations: receiving an input at a generator neural network trained for generating map item positions; generating, with the generator neural network, a probability of placing a map item for each subregion of a plurality of subregions of the region of the virtual map; and generating position data of map items for placement on the virtual map using the probability for each subregion. The input to the generator neural network comprises: map data comprising one or more channels of position information for at least a region of the virtual map, said one or more channels including at least one channel comprising road position information for the region; and a latent vector encoding a selection of a placement configuration.

Generating positions of map items for placement on a virtual map

Embodiments of the systems and methods described herein provide a virtual object aging system. The virtual object aging system can utilize artificial intelligence to modify virtual objects within a video game to age and/or deteriorate for a certain time period. The virtual object aging system can be used to determine erosion, melting ice, and/or other environmental effects on virtual objects within the game. The virtual object aging system can apply aging, rust, weathering, and/or other effects that cause persistent change to object meshes and textures.

Artificial intelligence based virtual object aging

Embodiments of the systems and methods described herein provide game terrain generation system that can generate height field data from a sketch of graphical inputs from a user via a graphical user interface. The game terrain generation system can use a model, such as a trained neural network, to apply macro and micro topological features on top of the height field data to generate game terrain data. The game terrain generation system can identify boundaries between different styles of terrain and generate transitions between the styles to create a more realistic terrain boundary.

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San Francisco, CA, United States of America

Han Liu