Livestream Scene Object Replacement for Seamless Product Switching
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Solution Overview
Problem
Current live shopping platforms lack the ability to seamlessly switch between products in real-time, considering user preferences and often result in manual editing with noticeable gaps, failing to provide a personalized experience.
Innovation Solution
A system that computes a scene description of a livestream, identifies target objects, and replaces them with personalized secondary objects using 2D to 3D conversion and back, ensuring seamless integration by leveraging photogrammetry and metadata, while allowing for scalability through pre-loading and cloud processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual video editing is used to switch between products, then the switching process can be completed, but the process is cumbersome and results in noticeable gaps in video frames
Solution Approach 1:
The patent replaces manual mechanical video editing with an automated computer vision system that uses object detection, tracking, and identification to automatically switch between products. The system captures video frames, detects product boundaries and features, identifies products using AI/ML models, and executes switches without manual intervention, thereby eliminating both the cumbersome editing process and the noticeable gaps that result from manual operations.
2Productivity
If a single product switch is applied to all viewers, then the livestream can be maintained, but user preferences are not considered resulting in a one-size-fits-all approach
Solution Approach 1:
The patent implements local quality by delivering customized product switches to different users based on their individual preferences, purchase history, and demographic characteristics. Instead of applying a uniform product switch to all viewers, the system analyzes each user's profile and selectively presents product switches that are most relevant to that specific user, thereby maintaining livestream efficiency while enabling personalized experiences.
Solution Approach 2:
The system performs preliminary actions by pre-analyzing user profiles, preferences, and purchase histories before the livestream begins. This advance preparation allows the system to ready personalized product switch recommendations for each user, so that when the livestream occurs, the system can immediately deliver customized product switches without delaying the broadcast or requiring real-time decision-making during the stream.
3Manufacturing precision
If real-time object replacement is implemented, then seamless switching can be achieved, but computational complexity and processing requirements increase significantly
Solution Approach 1:
The patent applies segmentation by breaking down the complex real-time object replacement task into distinct modular components: video frame capture, product boundary detection, product feature extraction, product identification using AI/ML models, product switch decision-making, and video frame rendering. Each module handles a specific aspect of the process independently, which reduces overall system complexity while maintaining seamless replacement quality through coordinated operation of these specialized sub-systems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, personalized product switching in livestreams, enhancing user engagement by eliminating video editing gaps and catering to individual viewer preferences.
Implementation Method 1
using 2D to 3D conversion and back, ensuring seamless integration by leveraging photogrammetry and metadata
Data Source
AI summary
Systems and methods are described for replacing an object being presented in a livestream with a secondary object that is personalized to a user that is consuming the livestream and rendering the livestream with the secondary object are described. The methods identify a target object in a livestream based on certain selection factors. A secondary object that is contextually related to the target object and selected based on selection factors is selected. A 2D-to-3D conversion of the scene description is performed to generate a 3D model. A replacement option is selected. The attributes of the secondary object based on its 3D model are matched with attributes of the target object based on its generated 3D model. Quality checks and scalability options are explored. The livestream is re-rendered with the secondary object having replaced the target object.