Video Production System Predicting Next Steps to Reduce Asset Selection Time
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Solution Overview
Problem
Existing video production systems face inefficiencies due to large libraries of digital assets, making it time-consuming for users to select appropriate assets, especially with the advent of generative asset creation, leading to a need for a more intuitive system that guides users through the video generation process while ensuring high-quality production.
Innovation Solution
A video production system that uses prediction models, including deep learning, to track the video's state and recommend next steps and assets based on metadata, suggesting asset types and instances to add, with context such as time and position, leveraging machine learning to infer optimal placement and generation of assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users browse large libraries of digital assets manually, then they can select from many options, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by predicting the next step in video generation before the user needs to select assets. The prediction model analyzes the current video state and pre-determines what assets are most likely needed, allowing the system to present pre-sorted or pre-selected assets rather than requiring users to browse entire libraries manually.
Solution Approach 2:
The prediction model acts as an intermediary between the user's video creation needs and the large asset library. It translates the current video state into predicted next steps and asset recommendations, serving as a mediator that filters and prioritizes assets based on contextual understanding rather than requiring direct user browsing of all options.
2Reliability
If the system provides comprehensive asset recommendations, then video quality improves, but the complexity of the recommendation system increases
Solution Approach 1:
The system segments the recommendation task into multiple specialized prediction models, each trained on specific aspects of video generation (e.g., scene transitions, asset types, temporal patterns). This segmentation allows each model to be simpler and more focused, while collectively they provide comprehensive recommendations without requiring a single overly complex system.
Solution Approach 2:
The system changes parameters by training prediction models on diverse video data with varying characteristics, and by adjusting recommendation parameters based on the specific video state, genre, and production context. This allows the system to adapt recommendations to different scenarios without requiring a completely different system architecture for each case.
3Measurement precision
If the system tracks and analyzes detailed video state metadata, then prediction accuracy improves, but the computational resources required increase
Solution Approach 1:
The system applies local quality by analyzing only the specific aspects of video state that are most relevant to predicting the next step, rather than uniformly processing all possible metadata. The prediction models focus on locally important features such as current scene content, temporal position, and asset relationships, reducing computational overhead while maintaining high prediction accuracy for the specific task at hand.
Data Source
AI summary
This disclosure relates to a system, method, and computer program for facilitating video creation by recommending next steps in a user interface for video creation. A video production system tracks the state of a video as a user makes changes to the video. As the user develops the video, the system predicts the next step in the video generation process and makes recommendations to the user based on this prediction. The system will recommend an asset type to add to the video and also suggest specific instances of the asset type to add to the video. The video production system leverages a number of prediction models. The models include a deep learning model that is trained on a large corpus of video material to predict a next step of a video based on a current state. The system greatly shortens the time needed for the production of digital video by recommending video assets automatically to the user. As the underlying model has been trained on a corpus of high quality data, this system will lead the user to create high quality video with the correct conventions, whilst still allowing creative direction.