Eyewear AR Content Recording with Layered Neural Processing
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Solution Overview
Problem
Existing technologies face challenges in efficiently capturing, processing, and sharing augmented reality (AR) content on power and resource-constrained electronic devices, particularly in bridging the gap between real and virtual environments, and enhancing user experiences with digital images under varying conditions.
Innovation Solution
A messaging system with an extensible graphical rendering pipeline architecture supports layering of post-processing operations, enabling efficient capturing and rendering of AR content on eyewear devices, utilizing neural networks for real-time image transformations and overlays, and integrating with messaging client applications for sharing and editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time image processing and neural network transformations are implemented on eyewear devices, then AR content rendering quality and interactivity are improved, but power consumption and computational load increase
Solution Approach 1:
The system divides AR content processing into multiple stages: capturing raw image data, applying neural network transformations, compositing virtual overlays, and rendering final output. Each stage is handled by dedicated processing modules, allowing parallel execution and optimized resource allocation to reduce overall power consumption while maintaining real-time performance
Solution Approach 2:
Neural network models are pre-trained and stored in memory before runtime. During AR content generation, the system performs inference using these pre-trained models rather than training them in real-time, significantly reducing computational load and power consumption while maintaining transformation quality
2Manufacturing precision
If multiple post-processing operations and virtual content overlays are applied, then AR content quality and user experience are improved, but processing latency increases
Solution Approach 1:
The system processes AR content frames at fixed time intervals using a frame rate controller, ensuring consistent latency bounds. By synchronizing processing operations to periodic frame updates rather than continuous processing, the system maintains rendering quality while preventing latency accumulation
Solution Approach 2:
The system processes multiple AR content layers in parallel across different computational dimensions (CPU, GPU, NPU) simultaneously, then composites them in the final rendering stage. This dimensional parallelization reduces sequential processing latency while maintaining comprehensive post-processing quality
3Adaptability or versatility
If comprehensive AR content capture and processing is implemented, then functionality and user experience are improved, but device complexity increases
Solution Approach 1:
The system employs a unified processing pipeline that handles multiple AR content types (image overlays, video annotations, 3D objects, text annotations) through the same neural network transformation and compositing architecture. This universal approach provides comprehensive functionality while avoiding the complexity of separate dedicated systems for each content type
Solution Approach 2:
The system introduces an AR content generator as an intermediary component that receives raw image data, applies neural network transformations, and outputs processed content for display. This intermediary layer abstracts the complexity of multiple processing operations, providing versatile AR functionality while simplifying the interface between capture and display subsystems
Data Source
AI summary
The subject technology sends captured image data and metadata to a first set of computing processes for processing. The subject technology receives, asynchronously from a second set of computing processes, second metadata. The subject technology detects a signal indicating a command to stop image data capturing. The subject technology, in response to the detected signal, generating a composite AR content item based on the metadata and the captured image data. The subject technology provides the composite AR content item for rendering for display on an eyewear device.