Methods and systems for interpolation of disparate inputs
AssigneeMagic Leap, Inc.
InventorGeoffrey Wedig
Life of this patent
Estimating the term…
Abstract
Systems and methods are provided for interpolation of disparate inputs. A radial basis function neural network (RBFNN) may be used to interpolate the pose of a digital character. Input parameters to the RBFNN may be separated by data type (e.g. angular vs. linear) and manipulated within the RBFNN by distance functions specific to the data type (e.g. use an angular distance function for the angular input data). A weight may be applied to each distance to compensate for input data representing different variables (e.g. clavicle vs. shoulder). The output parameters of the RBFNN may be a set of independent values, which may be combined into combination values (e.g. representing x, y, z, w angular value in SO(3) space).
Claims20
A method comprising:
- receiving input data comprising at least one angular component and at least one linear component;
- providing the input data as input to at least one neural network (NN) that has been trained to evaluate the at least one angular component differently than the at least one linear component;
- and
- receiving output data that is generated by the at least one NN based on the different evaluation of the at least one angular component and the at least one linear component.
A system comprising:
- at least one processor that executes at least one neural network (NN);
- and
- memory communicatively coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
- receiving input data comprising at least one angular component and at least one linear component;
- providing the input data as input to at least one neural network (NN) that has been trained to evaluate the at least one angular component differently than the at least one linear component;
- and
- receiving output data that is generated by the at least one NN based on the different evaluation of the at least one angular component and the at least one linear component.
Dependencies are inferred from the claim text.