Techniques for solving inverse kinematic problems using trained machine learning models
AssigneeAutodesk, Inc.
InventorsEvan Patrick Atherton, Dieu Linh Tran
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Abstract
In various embodiments, a computer animation application automatically solves inverse kinematic problems when generating object animations. The computer animation application determines a target vector based on a target value for a joint parameter associated with a joint chain and at least one of a target position or a target orientation for an end-effector associated with the joint chain. The computer animation application executes a trained machine learning model on the target vector to generate a predicted vector that includes data associated with multiple joint parameters associated with the joint chain.
Claims19
A computer-implemented method for automatically solving inverse kinematic problems when generating object animations, the method comprising:
- determining a first target vector based on a first target value for a first joint parameter associated with a first joint included in a joint chain and at least one of a first target pose or a first target orientation for an end-effector associated with the joint chain, wherein the joint chain includes a plurality of joints associated with a skeleton of an object being animated and the first target value for the first joint parameter comprises at least a maximum angle or a minimum joint angle for constraining a rotation of the first joint;
- and
- executing a trained machine learning model on the first target vector to generate a first predicted vector that includes data associated with a first plurality of joint parameters associated with the joint chain.
One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to automatically solve inverse kinematic problems when generating object animations by performing the steps of:
- determining a first target vector based on a first target value for a first joint parameter associated with a first joint included in a joint chain and at least one of a first target pose or a first target orientation for an end-effector associated with the joint chain, wherein the joint chain includes a plurality of joints associated with a skeleton of an object being animated and the first target value for the first joint parameter comprises at least a maximum angle or a minimum joint angle for constraining a rotation of the first joint;
- and
- executing a trained machine learning model on the first target vector to generate a first predicted vector that includes data associated with a first plurality of joint parameters associated with the joint chain.
A system comprising:
- one or more memories storing instructions;
- and
- one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
- determining a first target vector based on a first target value for a first joint parameter associated with a first joint included in a joint chain and at least one of a first target pose or a first target orientation for an end-effector associated with the joint chain, wherein the joint chain includes a plurality of joints associated with a skeleton of an object being animated and the first target value for the first joint parameter comprises at least a maximum angle or a minimum joint angle for constraining a rotation of the first joint;
- and
- executing a trained machine learning model on the first target vector to generate a first predicted vector that includes data associated with a first plurality of joint parameters associated with the joint chain.
Dependencies are inferred from the claim text.