Electronic Arts Inc.

Electronics Arts, Inc.

Yiwei Zhao

Navid Aghdaie

Mohsen Sardari

Harold Henry Chaput

Wolfram Sebastian Starke

Kazi Zaman

Han Liu

Kazi Atif-Uz Zaman

Jingwen Liang

Kenneth Alan Moss

Igor Borovikov

Maziar Sanjabi

Mathieu Guindon

Mingyi Shi

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

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.

Joint twist generation for animation

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

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 the systems and methods described herein provide a dynamic animation generation system that can apply a real-life video clip with a character in motion to a first neural network to receive rough motion data, such as pose information, for each of the frames of the video clip, and overlay the pose information on top of the video clip to generate a modified video clip. The system can identify a sliding window that includes a current frame, past frames, and future frames of the modified video clip, and apply the modified video clip to a second neural network to predict a next frame. The dynamic animation generation system can then move the sliding window to the next frame while including the predicted next frame, and apply the new sliding window to the second neural network to predict the following frame to the next frame.

Enhanced animation generation based on video with local phase

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

A method, computer-readable storage medium, and device for generating an animation sequence are disclosed. The method comprises: receiving an input animation sequence, wherein the input animation sequence comprises character position information over a series of frames and a first style tag; executing an encoder to process the input animation sequence to generate a compressed representation of the input animation sequence, wherein the compressed representation of the input animation sequence comprises a vector representing the input animation sequence; and executing a decoder to generate an output animation sequence, wherein executing the decoder is based on the compressed representation of the input animation sequence, wherein the output animation sequence comprises character position information over a series of frames, and wherein the output animation sequence is based on the input animation sequence and comprises a second style tag.

Systems and methods for supervised and unsupervised animation style transfer

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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Sunnyvale, CA, United States of America

Yiwei Zhao