US 12,277,662 B2
Object alignment techniques for animation
Hayko Riemenschneider, Zürich (CH); Clara Fernandez, Zürich (CH); Joan Massich, Zürich (CH); Christopher Richard Schroers, Uster (CH); Daniel Teece, Pasadena, CA (US); Rasmus Tamstorf, Los Angeles, CA (US); Charles Tappan, Los Angeles, CA (US); Mark Meyer, San Francisco, CA (US); and Antoine Milliez, Bern (CH)
Assigned to PIXAR, Emeryville, CA (US); and DISNEY ENTERPRISES, INC., Burbank, CA (US)
Filed by Pixar, Emeryville, CA (US); and Disney Enterprises, Inc., Burbank, CA (US)
Filed on Oct. 31, 2022, as Appl. No. 17/977,935.
Prior Publication US 2024/0144622 A1, May 2, 2024
Int. Cl. G06T 19/20 (2011.01); G06T 7/50 (2017.01); G06T 13/20 (2011.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01)
CPC G06T 19/20 (2013.01) [G06T 7/50 (2017.01); G06T 13/20 (2013.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30196 (2013.01); G06T 2210/56 (2013.01); G06T 2219/2004 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method for aligning objects for computer generated imagery, the method comprising performing, by a computer system:
obtaining a source object representation representing a source object and a target object representation representing a target object;
identifying a category of the source object and a category of the target object, wherein identifying the category of the source object and identifying the category of the target object comprises providing the object representations to a classifier machine learning model, wherein the classifier machine learning model is a neural network comprising:
an input layer for accepting an object representation corresponding to an object;
two or more mixed diffusion and convolution layers; and
an output layer for outputting a probability that the object is part of a particular category of objects;
based on the category of the source object and the category of the target object, selecting a feature extraction machine learning model from a set of feature extraction machine learning models for different object categories;
providing the source object representation and the target object representation as input to the selected feature extraction machine learning model;
generating, by the feature extraction machine learning model, information identifying parts of the source object, information identifying parts of the target object, a shape vector for the source object, and a shape vector for the target object; and
generating an aligned target object representation that is aligned with the source object representation and usable for animating the target object, the generating using an alignment machine learning model based on the information identifying parts of the source object, the information identifying parts of the target object, the shape vector for the source object, and the shape vector for the target object.