Crowdsourced Volumetric Video Assembly for Closed-Loop Case Study Capture
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Solution Overview
Problem
Existing methods for creating educational case study content using volumetric video technology are limited by the quality and quantity of directional videos, and the processing engine's effectiveness, which affects the immersive experience and usefulness of the content.
Innovation Solution
A system and method that utilizes crowdsourced media content from social media networks to identify and select videos that form closed loop contours, considering capture location, direction, and timing, to create volumetric video content tailored to educational case studies, which can be consumed in a Virtual Reality environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional volumetric video technology is used to create educational case study content, then the content can provide immersive three-dimensional viewing experiences, but the quality and usefulness are limited by the quantity and quality of directional videos available
Solution Approach 1:
The patent introduces a processing engine as an intermediary that automatically selects and combines directional videos from crowd-sourced content. This mediator resolves the contradiction by filtering the large quantity of available videos (parameter 26) to identify the specific subset needed to achieve high-quality volumetric video output (parameter 27), eliminating the need for manual selection while ensuring optimal content quality
Solution Approach 2:
The system enables automatic self-service by allowing the processing engine to autonomously evaluate, select, and assemble directional videos from crowd-sourced content based on predefined quality metrics and case study requirements. This self-service mechanism resolves the contradiction by automatically managing the relationship between video quantity and quality without requiring external intervention, thereby maintaining high content reliability while efficiently utilizing available volumetric data
2Reliability
If multiple directional videos are collected to improve volumetric video quality, then the immersive experience is enhanced, but the complexity of selecting and processing appropriate videos increases
Solution Approach 1:
The patent applies parameter changes by establishing specific quality metrics and selection criteria that the processing engine uses to evaluate directional videos. By defining concrete parameters such as video resolution, frame rate, temporal synchronization, and spatial coverage requirements, the system transforms the complex qualitative assessment into quantifiable parameter-based selection, thereby enhancing immersive experience while managing processing complexity through standardized evaluation criteria
Solution Approach 2:
The processing engine segments the complex task of volumetric video creation into distinct operational stages: retrieving candidate directional videos, evaluating them against quality parameters, selecting optimal clips, and assembling them into the final volumetric output. This segmentation resolves the contradiction by breaking down the complex processing workflow into manageable, automated steps that can be executed systematically, reducing overall system complexity while maintaining high output quality
3Quantity of substance
If crowd-sourced videos are used to increase the quantity of directional content, then more comprehensive coverage is achieved, but the difficulty of identifying videos that form closed loop contours increases
Solution Approach 1:
The processing engine implements feedback mechanisms by continuously evaluating retrieved directional videos against the closed loop contour requirement and adjusting its selection criteria accordingly. When videos that form proper closed loops are identified, the system reinforces this selection pattern; when insufficient closed loop structures are found, the engine modifies its retrieval parameters to prioritize videos with appropriate temporal and spatial characteristics. This feedback-driven approach resolves the contradiction by systematically navigating the large volume of crowd-sourced content to identify suitable closed loop structures
4Manufacturing precision
If strict quality requirements are applied to select directional videos, then the final volumetric video quality is improved, but the number of suitable videos from crowd-sourced content decreases
Solution Approach 1:
The system applies partial action by retrieving a large excess of candidate directional videos from crowd-sourced content, then selectively applying strict quality filters to identify the optimal subset. Rather than attempting to find the perfect video from a limited set, the system retrieves more videos than ultimately needed and uses automated processing to identify the best matches. This approach resolves the contradiction by decoupling the retrieval quantity from the final selection quantity, allowing strict quality requirements to be applied without limiting the initial search pool
Data Source
AI summary
In an approach for creating educational case study content based on crowdsourced media content, a processor receives a request for a video to be used with a case study and determines one or more requirements for the video. Based on the one or more requirements, the processor retrieves a plurality of crowdsourced videos from a social media network and determines at least one set of the plurality of crowdsourced videos that can form a closed loop contour. The processor further, based on the at least one set of the plurality of crowdsourced videos, creates the video to be used with the case study.