Context-Aware Ad Video Generation for Storyline-Consistent Insertion
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Solution Overview
Problem
Existing video advertising methods often insert prerecorded ads that are inconsistent with the context and storyline of the video stream, leading to an annoying viewer experience and reduced ad impact.
Innovation Solution
A computing device uses generative AI models, including image-to-text and text-to-video models, to generate new advertisement videos that align with the context and storyline of the primary video stream by capturing images, generating caption text, selecting compatible products or brands, and creating ad video clips that seamlessly integrate with the video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pre-recorded ads are inserted into video streams based on expected viewership, then ad selection is simplified and automation is improved, but ad consistency with video storyline deteriorates
Solution Approach 1:
The system performs preliminary analysis of the video stream content, context, and storyline before ad insertion. It extracts visual features, understands the narrative context, and pre-selects appropriate ads that match the video content, ensuring consistency while maintaining automation.
Solution Approach 2:
The system uses AI models to analyze the video content and provides feedback on ad suitability. The process continuously evaluates whether selected ads match the video storyline and context, enabling automated selection with high consistency through iterative refinement.
2Reliability
If context-aware ad selection is implemented, then ad storyline consistency is improved, but system complexity increases
Solution Approach 1:
The system introduces AI models and intermediate processing layers that act as mediators between the video content and ad selection. These intermediaries analyze video context, extract relevant features, and match them with appropriate ads, managing complexity through modular architecture.
Solution Approach 2:
The system segments the ad insertion process into distinct modules: video analysis, context understanding, ad matching, and insertion. Each module handles a specific aspect of the task, reducing overall system complexity through functional decomposition while maintaining high storyline consistency.
3Manufacturing precision
If manual video splicing with ads is performed, then ad insertion precision is improved, but productivity deteriorates
Solution Approach 1:
The system replaces manual mechanical video splicing with automated electronic processing. AI models and computer vision algorithms automatically analyze video content, determine optimal insertion points, and execute ad placement, achieving both high precision and improved productivity through automation.
Data Source
AI summary
Embodiments include methods for generating advertisement videos for insertion into a video stream to promote a product, service, or brand in a manner that is consistent with the context and storyline of the video stream before and at the time of ad insertion. Methods may include capturing an image from the video stream and generating caption text using an image-to-text description model. A product, service, or brand that is consistent with the context and storyline of the captured image is selected and ad video sequence description text is generated that includes descriptions and a storyline blending descriptions of the selected product, service, or brand with the context and storyline of the primary video stream. The ad video sequence description text is used to prompt a text-to-video generation model that generates a new advertisement video clip, which is inserted into the primary video stream before distribution to video content rendering devices.