3D Media Insertion in 2D Video Across Multiple Perspectives
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing advertising and product placement technologies in 2D video content struggle to maintain realism when switching between multiple perspectives and lack dynamic, context-aware insertion capabilities.
Innovation Solution
Generate a 3D representation of a 2D video scene, identify void spaces, prioritize them based on technical and business factors, and insert 3D media elements using camera tracking and machine learning, with a blockchain ledger to manage access and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional 2D advertising insertion methods are used, then implementation is simple, but realism is lost when switching between multiple perspectives
Solution Approach 1:
The patent transitions from 2D advertising insertion to 3D media element insertion by generating a 3D representation of the video scene. This allows advertisements to be placed in three-dimensional space rather than simply overlaid on 2D frames, maintaining realism when the camera perspective changes. The system identifies void spaces in the 3D scene graph and inserts 3D media elements that can be properly rendered from any camera angle.
2Adaptability or versatility
If product placement is done manually in production, then control over placement is high, but adaptability to different contexts and viewers is limited
Solution Approach 1:
The system dynamically inserts 3D media elements based on real-time analysis of the video scene and viewer context. Rather than static pre-placement, the system can adaptively choose where and how to insert advertisements based on factors such as viewer preferences, demographic information, and scene analysis. The prioritization of void spaces and selection of insertion points is performed dynamically during content delivery.
Solution Approach 2:
The system automatically performs scene analysis, void space identification, and media element selection without requiring manual intervention for each insertion. Machine learning models analyze the video content to identify suitable placement locations, and the system autonomously renders and inserts the 3D media elements, reducing the need for manual post-production work.
3Reliability
If multiple camera perspectives are used in video production, then scene realism is improved, but maintaining consistent advertising placement across perspectives becomes difficult
Solution Approach 1:
The system performs preliminary action by generating a 3D representation of the entire scene and pre-identifying void spaces before camera rendering. This 3D scene graph serves as a master blueprint that contains all spatial information about the scene. When camera perspectives change, the system can efficiently re-render the 3D media elements from new angles using the pre-established 3D model, rather than having to manually adjust placements for each camera angle.
Solution Approach 2:
The 3D scene graph acts as an intermediary between the original video content and the final rendered output with advertisements. Instead of directly manipulating 2D video frames for each camera perspective, the system uses the 3D scene graph as an intermediate representation that captures the spatial relationships of all objects. This intermediary model enables consistent advertising placement across multiple perspectives by allowing the system to query and render from any camera position in the 3D space.
4Productivity
If void spaces are identified and prioritized for insertion, then advertising effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The system changes parameters by prioritizing void spaces based on multiple factors including visibility duration, camera proximity, and scene importance. Rather than treating all void spaces equally, the system assigns priority scores to different insertion locations and selects the highest-priority spaces for media element placement. This parameter-based prioritization optimizes the balance between advertising effectiveness and processing efficiency by focusing computational resources on the most valuable insertion points.
Data Source
AI summary
A method for processing media content includes aggregating, by at least one hardware processor, a plurality of three-dimensional (3D) images to obtain a 3D volume representation of a plurality of two-dimensional (2D) images of media content. A 3D void space associated with the plurality of 2D images is segmented based on the 3D volume representation into a plurality of 3D void segments. A media element is inserted into at least one 3D void segment of the plurality of 3D void segments. At least one 2D image of the plurality of 2D images is modified to include the at least one 3D void segment.