Enhanced animation generation based on conditional modeling
AssigneeElectronic Arts Inc.
InventorsElaheh Akhoundi, Fabio Zinno
Life of this patent
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Abstract
Systems and methods are provided for enhanced animation generation based on conditional modeling. An example method includes accessing an autoencoder trained based on poses and conditional information associated with the poses, each pose being defined based on location information associated with joints, and the conditional information for each pose reflecting prior poses of the pose, with the autoencoder being trained to reconstruct, via a latent variable space, each pose based on the conditional information. Poses in a sequence of poses, are obtained via an interactive user interface, and the latent variable space is sampled. An output pose is generated based on the sampling, the output pose being included in the interactive user interface.
Claims20
A computer-implemented method comprising:
- accessing an autoencoder trained based on a plurality of poses of one or more real-world persons and conditional information associated with the poses, each pose being defined based on location information associated with a plurality of joints, and the conditional information for each pose reflecting one or more prior poses of the pose, wherein the autoencoder was trained to reconstruct, via a latent variable space, each pose based on the conditional information;
- obtaining, via an interactive user interface, a sequence of poses comprising a first pose and a second pose which is subsequent to the first pose, and sampling the latent variable space;
- and
- generating, based on the autoencoder for inclusion in the interactive user interface, an output pose based on the sampling and the sequence of poses, wherein the sequence of poses is updated to include the output pose.
Non-transitory computer storage media storing instructions that when executed by a system of one or more computers, cause the one or more computers to perform operations comprising:
- accessing an autoencoder trained based on a plurality of poses and conditional information associated with the poses, each pose being defined based on location information associated with a plurality of joints, and the conditional information for each pose reflecting one or more prior poses of the pose, wherein the autoencoder was trained to reconstruct, via a latent variable space, each pose based on the conditional information;
- obtaining, via an interactive user interface, a sequence of poses comprising a first pose and a second pose which is subsequent to the first pose, and sampling the latent variable space;
- and
- generating, based on the autoencoder for inclusion in the interactive user interface, an output pose based on the sampling and the sequence of poses, wherein the sequence of poses is updated to include the output pose.
A system comprising one or more computers and non-transitory computer storage media storing instructions that when executed by the one or more computers, cause the one or more computers to perform operations comprising:
- accessing an autoencoder trained based on a plurality of poses and conditional information associated with the poses, each pose being defined based on location information associated with a plurality of joints, and the conditional information for each pose reflecting one or more prior poses of the pose, wherein the autoencoder was trained to reconstruct, via a latent variable space, each pose based on the conditional information;
- obtaining, via an interactive user interface, a sequence of poses comprising a first pose and a second pose which is subsequent to the first pose, and sampling the latent variable space;
- and
- generating, based on the autoencoder for inclusion in the interactive user interface, an output pose based on the sampling and the sequence of poses, wherein the sequence of poses is updated to include the output pose.
Dependencies are inferred from the claim text.