AI Frame-by-Frame Object Mapping for Temporal-Geospatial Consistency
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Solution Overview
Problem
Creating videos that meet specific temporal and geographic constraints, such as filming a movie set in a different time period and location, is complex and costly, with potential for errors due to missed props or objects not aligning with the desired geospatial model during editing.
Innovation Solution
A computer-implemented method processes videos frame-by-frame, identifying and modifying objects that do not fit the geospatial model to ensure temporal and spatial consistency, using artificial intelligence and geospatial modeling to adjust objects in each frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional video filming and editing methods are used to meet specific temporal and geographic constraints, then production flexibility is limited and re-shooting is required when errors are detected, but production costs increase and production time extends
Solution Approach 1:
The patent creates a digital geospatial model (a virtual copy) of the target location and time period, which can be overlaid and integrated with actual video footage. This digital twin allows precise control over geospatial accuracy without requiring physical re-shooting, as modifications can be made in the digital domain.
Solution Approach 2:
The patent replaces traditional mechanical filming approaches (physical sets, props, and re-shooting) with a computational system that uses AI-based object recognition, geospatial modeling, and digital compositing to achieve the desired temporal and geographic accuracy.
2Manufacturing precision
If traditional video editing methods are used to correct objects not fitting the geospatial model, then editing time increases and human error persists, but productivity decreases
Solution Approach 1:
The system performs self-correction by automatically detecting objects that do not fit the geospatial model and generating modifications without requiring manual intervention. The AI-based object recognition and geospatial validation systems autonomously identify and correct discrepancies, improving both accuracy and efficiency.
Solution Approach 2:
The patent implements a feedback loop where the system continuously validates objects against the geospatial model, automatically detects mismatches, and generates corrections. This closed-loop system ensures high object alignment accuracy while maintaining productivity through automation.
3Manufacturing precision
If extensive re-shooting is performed to ensure all objects align with the desired geospatial model, then manufacturing precision improves, but loss of time increases and production costs rise
Solution Approach 1:
The patent performs preliminary validation of geospatial consistency during the initial video processing stage by comparing objects against the geospatial model. This early detection and correction approach prevents the need for time-consuming re-shooting later in the production process.
Solution Approach 2:
Instead of re-shooting physical scenes, the system creates and modifies digital copies of video frames, replacing objects that do not fit the geospatial model with synthetically generated or digitally inserted alternatives. This digital replication approach maintains geospatial consistency without the time cost of physical re-shooting.
Data Source
AI summary
A computer implemented method processes a video. A processor set identifies frames in the video. The processor set recognizes objects in a frame in the frames. The processor set modifies each of the objects in the frame that do not fit the geospatial model for the frames to form a revised frame for the video.