18244016. VIRTUAL OBJECT MACHINE LEARNING simplified abstract (Snap Inc.)

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VIRTUAL OBJECT MACHINE LEARNING

Organization Name

Snap Inc.

Inventor(s)

Xuehan Xiong of Los Angeles CA (US)

Zehao Xue of Los Angeles CA (US)

VIRTUAL OBJECT MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18244016 titled 'VIRTUAL OBJECT MACHINE LEARNING

Simplified Explanation

Abstract: A machine learning scheme is described that can be trained on a set of labeled training images of a subject in different poses, textures, and background environments. The scheme utilizes metadata stored as 3D models or rendered images of the subject to identify the labeled data and create a classification model. This model can accurately classify a depicted subject in various environments and poses.

Patent/Innovation:

  • Machine learning scheme trained on labeled training images of a subject in different poses, textures, and background environments.
  • Utilizes metadata stored as 3D models or rendered images of the subject.
  • Automatically identifies labeled data to create a classification model.
  • Classification model can accurately classify a depicted subject in various environments and poses.

Potential Applications:

  • Facial recognition systems for security purposes.
  • Virtual reality and augmented reality applications.
  • Human-computer interaction and gesture recognition.
  • Animation and gaming industries.
  • Medical imaging and diagnostics.

Problems Solved:

  • Overcomes the challenge of accurately classifying subjects in different poses, textures, and background environments.
  • Provides a solution for training machine learning models on labeled data with diverse variations.
  • Enables accurate identification and classification of subjects in various real-world scenarios.

Benefits:

  • Improved accuracy and reliability in classifying subjects in different environments and poses.
  • Enhanced performance of facial recognition systems and other related applications.
  • Increased efficiency in human-computer interaction and gesture recognition.
  • Enables realistic and immersive experiences in virtual reality and augmented reality.
  • Advances medical imaging and diagnostics by accurately identifying subjects in diverse scenarios.


Original Abstract Submitted

A machine learning scheme can be trained on a set of labeled training images of a subject in different poses, with different textures, and with different background environments. The label or marker data of the subject may be stored as metadata to a 3D model of the subject or rendered images of the subject. The machine learning scheme may be implemented as a supervised learning scheme that can automatically identify the labeled data to create a classification model. The classification model can classify a depicted subject in many different environments and arrangements (e.g., poses).

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