Electronic Arts Inc.

Jingwen Liang

Mohsen Sardari

Han Liu

Navid Aghdaie

Harold Henry Chaput

Kazi Zaman

Yiwei Zhao

Jesse Harder

Schaefer Mitchell

Gary Ng Thow Hing

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

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San Diego, CA, United States of America

Jingwen Liang