3D Scene Reconstruction for Natural Real-Time Content Insertion
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Solution Overview
Problem
Conventional methods fail to efficiently and naturally insert advertisements or content into 2D or 3D environments based on target audiences, and struggle with incomplete scans, object replacement, and animation in virtual and augmented reality settings, leading to unnatural content integration and inefficient robot interactions with environments.
Innovation Solution
A computer-implemented system that uses object image identifiers to facilitate selective content insertion into preexisting frames, processes bids to determine winning content, and generates processed media frames by integrating winning content into the environment, while also enabling animation and object manipulation to create natural-looking scenes and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to insert advertisements into 2D or 3D environments, then content placement can be achieved, but the content appears unnatural and foreign to the scene
Solution Approach 1:
The patent applies local quality by analyzing specific properties of the target environment (lighting conditions, spatial characteristics, scene context) and adapting the advertisement content to match those local properties. This ensures the advertisement naturally integrates into the specific scene rather than appearing as a generic insertion.
Solution Approach 2:
The system dynamically changes parameters of the advertisement content (such as lighting, color, position, orientation) based on the target environment's characteristics and the intended audience. This allows the same advertisement to appear natural in different contexts while maintaining its core message.
2Reliability
If conventional approaches are used for identifying and replacing objects in scenes, then object replacement can be performed, but the system cannot handle incomplete scans or generate natural-looking replacements
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing reference object data (including incomplete scans) in advance. When object replacement is needed, the system compares the target object with pre-stored references and generates replacements based on this pre-prepared data, reducing complexity during real-time operation.
Solution Approach 2:
The system creates accurate copies of reference objects (from complete or incomplete scans) and applies them as replacements in the target scene. The copying process includes transferring geometric data, textures, and material properties to ensure the replacement object looks natural and matches the scene's quality.
3Productivity
If real-time content insertion is implemented for targeted audiences, then advertising effectiveness improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-evaluating and categorizing advertisement content based on target audience profiles before real-time insertion. This pre-processing allows the system to quickly match and insert appropriate content in real-time without extensive processing during the actual insertion moment.
Solution Approach 2:
The system dynamically adjusts the level of processing based on real-time requirements. For time-critical insertions, it uses pre-processed content with minimal additional processing, while for less time-sensitive cases, it performs more comprehensive analysis to optimize the match between content and audience.
4Ease of operation
If conventional robot interaction methods are used, then basic navigation is possible, but robots cannot efficiently identify movable objects or interact with the environment
Solution Approach 1:
The patent replaces mechanical/physical interaction methods with sensor-based detection and computational analysis. Robots use sensors to detect object properties (weight, friction, geometry) and computational algorithms to determine movability, eliminating the need for physical probing and improving both precision and ease of interaction.
Data Source
AI summary
A computer-implemented visual input reconstruction system for enabling selective insertion of content into preexisting media content frames may include at least one processor configured to perform operations. The operations may include accessing a memory storing object image identifiers associated with objects and transmitting, to one or more client devices, an object image identifier. The operations may include receiving bids from one or more client devices and determining a winning bid. The operations may include receiving winner image data from a winning client device and storing the winner image data in the memory. The operations may include identifying, in a preexisting media content frame, an object insertion location. The operations may include generating a processed media content frame by inserting a rendition of the winner image data at the object insertion location in the preexisting media content frame and transmitting the processed media content frame to one or more user devices.