US 12,340,624 B2
Pose prediction for articulated object
Mohammad Sadegh Ali Akbarian, Cambridge (GB); Pashmina Jonathan Cameron, Cambridge (GB); Andrew William Fitzgibbon, Cambridge (GB); and Thomas Joseph Cashman, Cambridge (GB)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Jun. 13, 2022, as Appl. No. 17/806,718.
Claims priority of provisional application 63/268,904, filed on Mar. 4, 2022.
Prior Publication US 2023/0282031 A1, Sep. 7, 2023
Int. Cl. G06V 40/20 (2022.01); G06T 7/73 (2017.01); G06V 10/774 (2022.01)
CPC G06V 40/23 (2022.01) [G06T 7/73 (2017.01); G06V 10/774 (2022.01); G06V 40/28 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for predicting the pose of an articulated object, comprising:
receiving spatial information for n joints of the articulated object;
passing the spatial information for the n joints to a machine learning model previously trained to receive spatial information for n+m joints as input, wherein m>=1, and wherein previously training the machine learning model includes providing training input data to the machine learning model for a quantity of joints that is greater than n, and over a series of training iterations, progressively reducing the quantity of joints; and
receiving as output from the machine learning model a pose prediction for the articulated object based at least on the spatial information for the n joints, and without spatial information for the m joints.