IP Library › Granted Patent US 12,307,621
Granted Patent B2
US 12,307,621 · App. 18/513,258 · Granted May 20, 2025

Adjusting a digital representation of a head region

Inventors: Eric Sommerlade (Oxford, GB); Alexandros Neophytou (Oxford, GB)
Assignee: RealD Spark, LLC
G06T19/20G06N3/045G06N3/08G06T7/337G06T2207/20081G06T2219/2004G06T2219/2012G06T2219/2021
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Quick Facts
Patent No.
US 12,307,621
App. No.
18/513,258
Granted
May 20, 2025
Kind
B2
Abstract

Methods and devices for generating reference data for adjusting a digital representation of a head region, and methods and devices for adjusting the digital representation of a head region are disclosed. In some arrangements, training data are received. A first machine learning algorithm generates first reference data using the training data. A second machine learning algorithm generates second reference data using the same training data and the first reference data generated by the first machine learning algorithm.

Claims (42)

1. A method of adjusting a digital representation of a head region, the method comprising:

identifying a target patch in the digital representation of the head region, the target patch comprising a target feature of the digital representation of the head region;

deriving a feature vector from local descriptors of the target patch;

determining, by a first inferential model, a displacement field configured to modify a relative position of a visual element of the target feature within the target patch, responsive to inputting the feature vector into the first inferential model;

determining, by a second inferential model, editing instructions configured to modify the displaced visual elements within the target patch, responsive to inputting the feature vector and the displacement field into the second inferential model; and

adjusting the target patch in the digital representation of the head region based on the editing instructions.

2. The method of claim 1 , wherein the first inferential model or the second inferential model includes a machine learning algorithm.

3. The method of claim 1 , wherein the first inferential model or the second inferential model includes a lookup table.

4. The method of claim 1 , wherein the first inferential model is computationally more complex than the second inferential model.

5. The method of claim 1 , wherein the editing instructions are configured to modify a pixel intensity of at least a portion of the displaced visual elements.

6. The method of claim 1 , wherein the editing instructions are configured to modify a color of at least a portion of the displaced visual elements.

7. The method of claim 1 , wherein the target feature includes at least a portion of an eye, and the operation of adjusting adjusts a gaze direction of the at least the portion of the eye within the target patch.

8. The method of claim 7 , wherein the adjusted gaze direction is substantially normal to the digital representation of the head region.

9. The method of claim 7 , wherein the operation of adjusting comprises:

retrieving data representing a position of an observer relative to a display device viewable by the observer, wherein the gaze direction is adjusted based on the retrieved data representing the position.

10. The method of claim 1 , wherein the target feature is at least a portion of a bodily feature selected from a group of a nose, a chin, a neck, or a hair region, and the operation of adjusting adjusts the bodily feature within the target patch.

11. The method of claim 1 , further comprising:

retraining the second inferential model at least at an input data frame rate of a real-time video sequence including the digital representation of the head region.

12. The method of claim 1 , wherein the editing instructions are provided in a compressed representation.

13. The method of claim 1 , wherein the digital representation of the head region comprises depth information of the head region.

14. The method of claim 1 , further comprising:

capturing, by a sensor, the digital representation of the head region, wherein the operation of identifying is responsive to the operation of capturing.

15. The method of claim 1 , further comprising:

displaying, by a digital display, the digital representation of the head region modified by the adjusted target patch.

16. The method of claim 1 , further comprising:

receiving, from a first computing system over a network, the digital representation of a head region, wherein the operation of identifying is responsive to the operation of receiving; and

transmitting, to a second computing system over the network, the digital representation of the head region including the adjusted target patch, wherein the operations of receiving and transmitting are elements of a real-time video exchange between the first computing system and the second computing system.

17. A computing system configured to adjust a digital representation of a head region, the computing system comprising:

one or more hardware processors;

a target patch identifier executable by the one or more hardware processors and configured to identify a target patch in the digital representation of the head region, the target patch comprising a target feature of the digital representation of the head region;

a feature vector deriver executable by the one or more hardware processors and configured to derive a feature vector from local descriptors of the target patch;

a displacement field determiner executable by the one or more hardware processors and configured to determine, by a first inferential model, a displacement field configured to modify a relative position of a visual element of the target feature within the target patch, responsive to inputting the feature vector into the first inferential model;

an editing instructions determiner executable by the one or more hardware processors and configured to determine, by a second inferential model, editing instructions configured to modify the displaced visual elements within the target patch, responsive to inputting the feature vector and the displacement field into the second inferential model; and

target patch adjuster executable by the one or more hardware processors and configured to adjust the target patch in the digital representation of the head region based on the editing instructions.

18. The computing system of claim 17 , wherein the editing instructions are configured to modify a pixel intensity of at least a portion of the displaced visual elements.

19. One or more processor-readable non-transitory storage media embodied with instructions for executing on a computing device a process of adjusting a digital representation of a head region, the process comprising:

identifying a target patch in the digital representation of the head region, the target patch comprising a target feature of the digital representation of the head region;

deriving a feature vector from local descriptors of the target patch;

determining, by a first inferential model, a displacement field configured to modify a relative position of a visual element of the target feature within the target patch, responsive to inputting the feature vector into the first inferential model;

determining, by a second inferential model, editing instructions configured to modify the displaced visual elements within the target patch, responsive to inputting the feature vector and the displacement field into the second inferential model; and

adjusting the target patch in the digital representation of the head region based on the editing instructions.

20. The one or more processor-readable non-transitory storage media of claim 19 , wherein the editing instructions are configured to modify a pixel intensity of at least a portion of the displaced visual elements.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 7, 2024
From: NEOPHYTOU, ALEXANDROS; SOMMERLADE, ERIC
To: REALD SPARK, LLC
Reel/Frame 067435/0410 →
Continuity (6)
Continuation 17572964 · Jan 11, 2022
Continuation 16937254 · Jul 23, 2020
Continuation 16057566 · Aug 7, 2018
Provisional Application 62543587 · Aug 10, 2017
Provisional Application 62542661 · Aug 8, 2017
Related Publication 20240242458A1 · Jul 18, 2024
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