Automated Virtual Product Placement in Video Frames
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Solution Overview
Problem
Current virtual product placement in videos is manual and inefficient, leading to stale advertising, limited revenue streams for content creators, and challenges in maintaining audience relevance, as well as impacting production schedules and storytelling.
Innovation Solution
An automated computer vision pipeline using machine learning models to identify suitable placement locations, handle occlusion, and render virtual products with natural lighting, allowing for real-time, non-intrusive, and consistent placement of advertisements in videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual virtual product placement is used, then placement accuracy and realism can be maintained, but production time increases and efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical placement processes with an automated computer vision system that uses machine learning models to detect surfaces and render virtual products. The system automatically identifies placement locations, handles occlusion, and renders products with natural lighting, eliminating the need for manual intervention while maintaining placement accuracy.
Solution Approach 2:
The system performs self-service by automatically detecting its own placement opportunities through computer vision, selecting appropriate locations, and executing the rendering without human intervention. The machine learning model autonomously analyzes video frames, identifies suitable surfaces, and coordinates the virtual product placement process independently.
2Productivity
If automated placement is implemented, then productivity increases, but placement realism and natural lighting may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously analyzes the video content, detects lighting conditions, and adjusts the virtual product rendering in real-time. The system receives feedback about the environment and automatically adapts the placement to maintain realism and natural lighting appearance.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting rendering parameters such as lighting, color, and shadow based on the detected environment. The system modifies these parameters to match the natural lighting conditions of the video scene, ensuring that automated placements appear realistic and integrated into the original content.
3Adaptability or versatility
If manual placement is used, then placement customization is possible, but revenue streams become limited and stale
Solution Approach 1:
The system enables dynamic placement customization by allowing virtual products to be inserted and modified in real-time during video processing. The automated system can adapt to different video contents, scenes, and lighting conditions, providing versatile placement options that can be customized for different products and audiences without manual intervention.
Solution Approach 2:
The machine learning model performs preliminary analysis of the video content, identifying potential placement locations and conditions before the actual rendering occurs. This preliminary action enables the system to prepare and optimize placement parameters in advance, allowing for customized virtual product integration that maintains high productivity and revenue potential.
4Productivity
If virtual products are inserted into video, then advertising revenue increases, but viewing experience may be disrupted
Solution Approach 1:
The system applies local quality by carefully selecting placement locations that are visually consistent with the surrounding environment. The machine learning model analyzes the local context of each potential placement site, considering lighting, texture, and spatial relationships, to ensure that virtual products blend naturally with the video content and do not disrupt the viewing experience.
Solution Approach 2:
The patent converts the potential harm of disruptive advertising by using computer vision to detect and avoid problematic placement locations. The system identifies areas where virtual products would be most visually intrusive and excludes those from consideration, instead selecting locations that naturally integrate with the content, thereby transforming the challenge of maintaining viewing experience into a benefit for ad effectiveness.
Data Source
AI summary
Techniques for a computer-implemented service for virtual product placement in video frames are described. According to some embodiments, a computer-implemented method includes receiving, at a virtual product placement service, a request to place a two-dimensional image of a virtual product into a video, identifying, by a machine learning model of the virtual product placement service, a surface depicted in the video for insertion of the two-dimensional image of the virtual product, inserting, by the virtual product placement service, of the two-dimensional image of the virtual product into one or more frames of the video onto the surface to generate a video including the virtual product, and transmitting the video including the virtual product to a viewer device or a storage location.