US 11,995,771 B2
Automated weighting generation for three-dimensional models
John L. Gibbs, Athens, GA (US); Benjamin Robert Flanders, Tucker, GA (US); and Dylan Scott Pozorski, Watertown, WI (US)
Assigned to UNIVERSITY OF GEORGIA RESEARCH FOUNDATION, INC., Athens, GA (US)
Filed by University of Georgia Research Foundation, Inc., Athens, GA (US)
Filed on Jun. 16, 2022, as Appl. No. 17/842,112.
Claims priority of provisional application 63/211,620, filed on Jun. 17, 2021.
Prior Publication US 2022/0406016 A1, Dec. 22, 2022
Int. Cl. G06T 17/20 (2006.01); G06N 3/04 (2023.01); G06T 19/20 (2011.01)
CPC G06T 17/205 (2013.01) [G06N 3/04 (2013.01); G06T 19/20 (2013.01); G06T 2219/2016 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A system, comprising:
a computing device comprising a processor and a memory; and
machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:
receive a first model weightings matrix;
adjust the number of rows in the first model weightings matrix to generate an adjusted model weightings matrix with a number of rows that matches an input number of rows for a machine-learning model, each column in the adjusted model weightings matrix representing a bone of a three-dimensional model and each row in the adjusted model weightings matrix representing a vertex of a mesh applied to the three-dimensional model;
apply the machine learning model to the adjusted model weightings matrix to generate an output model weightings matrix; and
generate a second model weightings matrix by adjusting the number of rows of the output model weightings matrix to match the number of rows of the first model weightings matrix.