Generating compound colliders
AssigneeSony Interactive Entertainment Inc.
InventorPhilip Cockram
Life of this patent
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Abstract
A method of generating a compound collider for a given object, the method comprising the steps of: receiving a mesh of the given object, the mesh comprising at least one concave portion; and generating a compound collider representative of at least the concave portion of the mesh, in which the step of generating comprises: inputting the mesh to a machine learning model trained to generate a respective compound collider based on an input mesh comprising at least one concave portion, and using the trained machine learning model to generate the compound collider representative of at least the concave portion of the mesh.
Claims20
A computer-implemented method of generating a compound collider for a given object, the method comprising:
- receiving, by one or more processors, a mesh of the given object, the mesh comprising at least one concave portion;
- receiving, by the one or more processors, information representative of a bone hierarchy of the given object;
- generating, by the one or more processors, a compound collider representative of the at least one concave portion of the mesh, wherein the generating comprises:
- inputting the mesh and the information representative of the bone hierarchy to a machine learning model,
- wherein the machine learning model has been trained to learn a correspondence between an input mesh and an input representative of an input bone hierarchy to generate a respective compound collider configured to move with the input bone hierarchy;
- and
- using the trained machine learning model to generate the compound collider based on a learned correspondence between the mesh of the given object and the information representative of the bone hierarchy of the given object, the generated compound collider being configured to move with the bone hierarchy of the given object.
A system comprising:
- one or more computer processors;
- and
- one or more storage devices storing instructions that, when executed by the one or more computer processors, cause the one or more computer processors to perform operations for generating a compound collider for a given object, the operations comprising:
- receiving a mesh of the given object, the mesh comprising at least one concave portion;
- receiving information representative of a bone hierarchy of the given object;
- generating a compound collider representative of the at least one concave portion of the mesh, wherein the generating comprises:
- inputting the mesh and the information representative of the bone hierarchy to a machine learning model,
- wherein the machine learning model has been trained to learn a correspondence between an input mesh and an input representative of an input bone hierarchy to generate a respective compound collider configured to move with the input bone hierarchy;
- and
- using the trained machine learning model to generate the compound collider based on a learned correspondence between the mesh of the given object and the information representative of the bone hierarchy of the given object, the generated compound collider being configured to move with the bone hierarchy of the given object.
One or more non-transitory computer-readable storage media storing instructions that, when executed by one or more computer processors, cause the one or more computer processors to perform operations for generating a compound collider for a given object, the operations comprising:
- receiving a mesh of the given object, the mesh comprising at least one concave portion;
- receiving information representative of a bone hierarchy of the given object;
- generating a compound collider representative of the at least one concave portion of the mesh, wherein the generating comprises:
- inputting the mesh and the information representative of the bone hierarchy to a machine learning model,
- wherein the machine learning model has been trained to learn a correspondence between an input mesh and an input representative of an input bone hierarchy to generate a respective compound collider configured to move with the input bone hierarchy;
- and
- using the trained machine learning model to generate the compound collider based on a learned correspondence between the mesh of the given object and the information representative of the bone hierarchy of the given object, the generated compound collider being configured to move with the bone hierarchy of the given object.
Dependencies are inferred from the claim text.