XR Avatar Expression Inference from Body Pose and Context
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Solution Overview
Problem
Existing AI systems struggle to accurately infer facial expressions from body gestures, especially in dynamic environments where only the face is visible, lacking the ability to integrate contextual and biometric data for realistic avatar representation.
Innovation Solution
A system utilizing an XR headset with AI models trained on facial expressions and body poses, combined with biometric data and social context, to drive plausible facial expressions based on body motions, audio, and environmental factors, enabling immersive experiences in augmented and virtual reality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI systems use only facial data for expression recognition, then the system can operate with simple hardware (standard webcam), but the accuracy and realism of expression inference deteriorates when only the face is visible
Solution Approach 1:
The patent merges multiple data sources (body pose data from depth cameras, facial data from webcams, and contextual information) into a unified expression recognition system. The AI model integrates these diverse inputs to infer facial expressions more accurately, resolving the contradiction by combining simple facial analysis with additional body gesture cues that provide contextual information about emotional state.
Solution Approach 2:
The patent introduces body pose data as an intermediary that bridges the gap between visible body gestures and facial expression inference. When facial data is limited or unavailable, the system uses body pose information as a mediator to infer expressions, improving accuracy without requiring direct facial capture in all scenarios.
2Measurement precision
If the system integrates multiple data sources (body pose, facial data, contextual info), then expression inference accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the expression recognition system into separate modules: a body pose estimation module (using depth cameras), a facial analysis module (using webcams), and an AI inference module that integrates both. This segmentation allows each component to be optimized independently while working together, managing overall system complexity through modular architecture.
Solution Approach 2:
The AI model serves multiple functions by processing different input types (body pose data, facial data, contextual information) and producing unified expression predictions. This multi-functionality consolidates what would otherwise be separate systems into a single versatile expression recognition platform, improving accuracy while controlling complexity through shared processing logic.
3Reliability
If the system uses body gesture data to infer facial expressions, then avatar realism improves, but the ability to detect expressions deteriorates when body gestures are not visible
Solution Approach 1:
The patent implements a dynamic expression inference system that adapts its approach based on available data. When body gestures are visible, the system uses them to enhance expression inference and improve avatar realism. When body gestures are not visible, the system dynamically switches to relying more on facial data, maintaining reliable expression detection across varying environmental conditions.
Solution Approach 2:
The system changes its operational parameters based on data availability. The AI model adjusts the weightings of different input sources (body pose vs. facial data) depending on what is visible in the current frame. This parameter adjustment allows the system to maintain high realism when possible while ensuring reliable detection when body gestures are not visible, balancing quality and robustness.
Data Source
AI summary
A device of the subject technology comprises a extra-reality (XR) headset including a processor configured to execute machine-learning (ML) instructions, memory configured to store a first set of data and a communications module configured to access a cloud storage including a second set of data. The ML instructions are configured to train an artificial-intelligence (AI) model to infer facial expressions based on at least one of the first set of data or the second set of data.