US 12,033,262 B2
Learning character model animations with a layer-wise mixture-of-experts network
Zhaoming Xie, Palo Alto, CA (US); Wolfram Sebastian Starke, Edinburgh (GB); and Harold Henry Chaput, Castro Valley, CA (US)
Assigned to Electronic Arts Inc., Redwood City, CA (US)
Filed by Electronic Arts Inc., Redwood City, CA (US)
Filed on Mar. 31, 2022, as Appl. No. 17/657,469.
Prior Publication US 2023/0316615 A1, Oct. 5, 2023
Int. Cl. G06T 13/40 (2011.01); A63F 13/57 (2014.01); G06N 20/00 (2019.01); G06T 13/80 (2011.01)
CPC G06T 13/40 (2013.01) [A63F 13/57 (2014.09); G06N 20/00 (2019.01); G06T 13/80 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A system, comprising:
one or more processors; and
one or more computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
determining, from among a plurality of edges from a first node of a control graph to respective other nodes of the control graph, a selected edge from the first node to a selected node, wherein the plurality of edges includes a first edge from the first node to a second node of the control graph and a second edge from the first node to a third node of the control graph, and the determining of the selected edge uses a random selection algorithm;
determining controls for an animated model in a simulation based at least in part on the selected edge, control data associated with the selected node, a current simulation state of the simulation, and a machine learned algorithm, wherein the machine learned algorithm includes a layer-wise mixture-of-experts (MOE) network comprising a plurality of linear MOE layers arranged in series with a shared gating network;
determining an updated simulation state of the simulation based at least in part on the controls for the animated model; and
adapting one or more parameters of the machine learned algorithm based at least in part on the updated simulation state and a desired simulation state.