AI Encapsulated Video Generation With Modular Media And Object Rules
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Solution Overview
Problem
Existing video generation technologies lack the ability to create customizable and encapsulated video content that can be dynamically tailored to user preferences and environmental context, leading to a one-size-fits-all approach that fails to provide personalized and varied video experiences.
Innovation Solution
The use of AI models trained on video rules, media unit rules, object parameters, and design rules to generate encapsulated video files, which can be customized based on user input, environmental context, and user profiles, allowing for dynamic or static presentation of media units and objects with specific customization rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing video generation technologies are used, then video content can be generated, but the video content cannot be customized or tailored to user preferences and environmental context
Solution Approach 1:
The video generation system is segmented into multiple independent AI models, each responsible for specific aspects: video rules model, media unit rules model, object parameters model, and design rules model. This segmentation allows each model to be trained and optimized independently for specific customization tasks, enabling flexible combination and reuse of models to create personalized video content without requiring complete system redesign.
Solution Approach 2:
The AI models are designed with universal applicability to handle multiple types of video customization tasks. The models can process different input formats (user text, scripts, feedback) and generate various output types (video rules, media unit selections, object parameters, design layouts). This multi-functionality allows the same model architecture to serve diverse customization needs across different video generation scenarios.
2Adaptability or versatility
If multiple AI models are used for different aspects of video generation, then customization capability is improved, but system complexity increases
Solution Approach 1:
The system divides video generation into distinct functional segments handled by specialized AI models: video rules model for structural constraints, media unit rules model for content selection, object parameters model for visual properties, and design rules model for layout composition. This segmentation enables independent training and optimization of each model for its specific task, improving overall personalization capability while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system introduces intermediary components that coordinate between multiple AI models: a unified model trainer that manages training data distribution across models, and a video generation orchestrator that integrates outputs from different models. These intermediaries simplify the interaction complexity between models, allowing them to work together seamlessly without requiring complex direct communication protocols between each model pair.
3Manufacturing precision
If AI models are trained with user feedback, then video generation accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The training process is segmented into separate training pipelines for each AI model, allowing parallel training execution. Each model (video rules, media unit rules, object parameters, design rules) receives targeted training data and feedback specific to its function, enabling simultaneous training without sequential dependencies. This segmentation reduces total training time while maintaining accuracy through specialized training for each model's specific task.
Solution Approach 2:
The system implements iterative training where models are trained incrementally with batches of user feedback rather than requiring complete retraining. The unified model trainer can selectively update specific models or parameters based on incoming feedback, performing partial training actions that improve accuracy progressively without the time cost of complete retraining cycles. This approach allows continuous improvement while minimizing time loss.
Data Source
AI summary
The present invention provides a method for generating video template using AI model implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the steps of:Training Ai model to generate encapsulated video template from user input wherein the encapsulated video is defined by video rules, media unit rules and parameters and object's parameters;Applying trained AI model to generate encapsulated video based on user input by determining video rules, media unit rules and parameters and object's parameters;