MACHINE LEARNING FOR ANIMATRONIC DEVELOPMENT AND OPTIMIZATION

Number: 20250117536
Published: 2025-04-10
Filed: 2024-12-17
Assignee: DISNEY ENTERPRISES, INC.
Inventors: MITCHELL; Kenneth J. et al.
CPC: G06F30/17; G06F30/20; G06F30/27; G06N20/00; G06N3/0464; G06N3/0475; G06N3/08; G06N3/09; G06N3/091; G06N3/094; G06T13/40; G06T17/20
Verdict: Medium Notable software
Source: Google Patents · FreePatentsOnline

The keeper's note

ML-based animatronic development/optimization technique (continuation).

Abstract

Techniques for animatronic design are provided. A plurality of simulated meshes is generated using a physics simulation model, where the plurality of simulated meshes corresponds to a plurality of actuator configurations for an animatronic mechanical design. A machine learning model is trained based on the plurality of simulated meshes and the plurality of actuator configurations. A plurality of predicted meshes is generated for the animatronic mechanical design, using the machine learning model, based on a second plurality of actuator configurations. Virtual animation of the animatronic mechanical design is facilitated based on the plurality of predicted meshes.

Background

BACKGROUND

The present disclosure generally relates to machine learning, and more specifically, to aiding animatronic design using machine learning.

Developing an animatronic is an expensive and time-consuming process. Typically, existing techniques require construction of electronic and mechanical machinery (e.g., actuators and a mechanical assembly, such as a rigid frame or skeleton), along with construction and attachment of an artificial skin to complete the animatronic. Further, animation software must be prepared to drive the motion of the animatronic. This takes significant time and effort, and it is only after all of these efforts are completed that designers can view how the animatronic looks in motion and in various poses. Redesign or modification of the animatronic at this stage is difficult and expensive, often requiring restarting the entire process. Existing attempts to facilitate the development process using computer-aided design are typically either inadequately accurate or excessively compute-intensive, rendering them unsuitable for practical use. SUMMARY

According to one embodiment of the present disclosure, a method is provided. The method includes generating a first plurality of simulated meshes using a physics simulation model, wherein the first plurality of simulated meshes corresponds to a first plurality of actuator configurations for an animatronic mechanical design. The method further includes training a machine learning model b

Claims

1. A computer-implemented method for generating an animatronic mesh, the method comprising: inputting, into a set of input neurons included in an input layer of a machine learning model, a set of actuator configurations for a set of actuators in an animatronic design; generating, via execution of the machine learning model based on the set of actuator configurations, a first plurality of positions for a first plurality of vertices in the animatronic mesh; and outputting the animatronic mesh based on the first plurality of positions. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: inputting, into a set of input neurons included in an input layer of a machine learning model, a set of actuator configurations for a set of actuators in an animatronic design; generating, via execution of the machine learning model based on the set of actuator configurations, a first plurality of positions for a first plurality of vertices in an animatronic mesh; and outputting the animatronic mesh based on the first plurality of positions. || 20. A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of: inputting, into a set of input neurons included in an input layer of a machine learning model, a set of actuator configurations for a set of actuators in an animatronic design; generating, via execution of the machine learning model based on the set of actuator configurations, a first plurality of positions for a first plurality of vertices in an animatronic mesh; and outputting the animatronic mesh based on the first plurality of positions.