Attention Map Based Static Scene Generation
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Solution Overview
Problem
Existing digital camera technologies do not effectively generate panoramic views of locations by prioritizing user attention, leading to suboptimal photo selection and combination for creating static scenes.
Innovation Solution
A method and system that collect photos from devices capable of tracking user gazes, generate attention maps based on gaze information, and combine photos into panoramic models of locations, selecting and replacing images based on criteria like resolution, quality, and attention levels to create dynamic and improved static scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If photos are combined into a panorama without prioritizing user attention, then the panorama can be generated quickly, but the quality and engagement of the static scene deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting attention information and generating an attention map before combining photos into a panorama. This pre-processing step identifies which objects and regions deserve higher priority, enabling subsequent photo selection to focus on attention-worthy content, thus improving scene quality without significantly impacting overall processing efficiency
Solution Approach 2:
The system applies local quality by differentiating the importance of different regions in the panorama based on user attention. High-attention regions receive priority in photo selection and combination, while low-attention regions are processed with standard criteria. This selective approach ensures quality improvement in critical areas without uniformly increasing processing complexity across the entire panorama
2Manufacturing precision
If multiple photos are collected and processed with attention-based selection, then the static scene quality improves, but the complexity of the system increases
Solution Approach 1:
The system introduces an attention map as an intermediary data structure that bridges raw attention information and photo selection decisions. This intermediate representation simplifies the complexity by providing a clear, structured way to query and filter photos based on attention priorities, avoiding the need for complex direct analysis of raw gaze data during photo combination
Solution Approach 2:
The system segments the photo collection and combination process into distinct stages: attention information collection, attention map generation, photo selection based on attention criteria, and panorama combination. This segmentation allows each module to be independently optimized and managed, reducing overall system complexity while maintaining high-quality output
3Measurement precision
If gaze tracking devices are used to collect attention information, then photo selection accuracy improves, but the cost and complexity of hardware increases
Solution Approach 1:
The system is designed to work with multiple types of attention information sources, including but not limited to gaze tracking devices. The attention map generation module can process data from various sources (eye trackers, head trackers, or even heuristic methods), making the system universally applicable and allowing deployment options that balance precision requirements against hardware complexity and cost
Data Source
AI summary
Implementations generally relate to generating static scenes. In some implementations, a method includes collecting photos associated with objects in at least one location. The method also includes collecting attention information associated with one or more of the objects. The method also includes generating an attention map based on the attention information. The method also includes generating a model of the at least one location based on the photos and the attention map.