Dynamic Video Performance Replacement via Machine Learning
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Solution Overview
Problem
Creating effective short-form videos for marketing products is a complex process that requires multiple rounds of recording and editing, and selecting the right narrator or host is crucial for success, but existing methods are inefficient and time-consuming.
Innovation Solution
The technique involves isolating and replacing the performance of a first individual in a short-form video with a second individual using machine learning, allowing for dynamic augmentation based on viewer interactions, including switching audio content to match the voice of the second individual, to create a more engaging and effective livestream event.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional methods of recording and editing short-form videos are used, then video quality can be maintained, but the production time and complexity increase significantly
Solution Approach 1:
The patent uses machine learning to generate a synthetic replica of the first individual's performance, capturing facial expressions, gestures, and voice characteristics. This synthetic copy can be rapidly generated and modified without requiring actual re-recording, thus maintaining video quality while dramatically reducing production time
Solution Approach 2:
The system performs preliminary analysis and extraction of performance characteristics from the original video before actual replacement is needed. By pre-processing the source material and creating reusable synthetic models, the system prepares all necessary components in advance, enabling rapid video generation when needed
2Adaptability or versatility
If a specific narrator or host is selected for a short-form video, then the video can be optimized for target audience, but the adaptability to different viewer interactions is reduced
Solution Approach 1:
The patent creates a dynamic system where the synthetic performance can be adaptively modified based on real-time viewer interactions, polls, and comments. The machine learning model allows the video content to dynamically adjust the narrator's responses and interactions while maintaining consistent character characteristics, enabling audience targeting without fixed scripting
Solution Approach 2:
The synthetic performance model acts as an intermediary between the original video content and the final customized output. This intermediate representation layer allows easy transformation into different languages, styles, or audience-specific versions without requiring complex re-editing of the original material
3Productivity
If multiple rounds of recording and editing are performed to create effective short-form videos, then the marketing effectiveness can be improved, but the resource consumption and production cost increase
Solution Approach 1:
By creating a synthetic copy of the performer's characteristics and performance patterns, the system eliminates the need for multiple physical recording sessions. The same synthetic model can be reused across multiple video productions, significantly reducing the energy and resources required for each new video while maintaining consistent quality and effectiveness
Solution Approach 2:
The system allows rapid modification of video parameters such as language, tone, pacing, and specific content elements by adjusting the synthetic performance model's parameters rather than re-recording. This enables multiple versions to be generated from a single recording session, improving productivity while reducing resource consumption
Data Source
AI summary
Disclosed embodiments provide techniques for augmented performance replacement in a short-form video. A short-form video is accessed, including a performance by a first individual. Using one or more processors, the performance of the first individual is isolated. Specific elements of the performance including gestures, clothing, expressions, and accessories are included in the isolation process. An image of a second individual is retrieved and information on the second individual is extracted from the image. A second short-form video is created by replacing the performance of the first individual with the second individual. The second short-form video is augmented based on viewer interaction. The augmenting of the second short-form video occurs dynamically. The augmenting includes additional audio content based on comments, responses to live polls or surveys, or questions and answers from viewers. The augmenting includes switching audio content in the second short-form video with additional audio content.