Game Summary Video Selection Through Audio, Video, and Chat Analysis
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Solution Overview
Problem
Generating an effective summary video of computer simulations automatically is difficult, and manual summarization is time-consuming.
Innovation Solution
A machine learning engine processes audio-video data using multiple modalities, including audio, video, and chat text, to identify candidate segments for a concise video summary by integrating models for acoustic events, speech features, scene changes, and text analysis to select relevant segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automatic summarization is used, then time consumption is reduced, but summarization quality is poor
Solution Approach 1:
The video is segmented into multiple candidate segments based on audio events, scene changes, and chat messages. Each segment is independently evaluated and scored to determine its relevance for the summary, allowing the system to construct a high-quality summary from multiple potential segments rather than relying on a single continuous extraction
Solution Approach 2:
The system merges multiple data modalities (audio, video, chat text) and combines their respective features and scores to create a comprehensive evaluation of candidate segments. The final summary is generated by integrating information from all modalities, producing a summary that reflects the full context of the original video
2Manufacturing precision
If manual summarization is used, then summarization quality is high, but time consumption is high
Solution Approach 1:
The system performs self-service by automatically analyzing the video content through multiple modalities and generating the summary without requiring human intervention. The automated pipeline processes audio events, video frames, and chat messages to independently produce a summary that mirrors what a human analyst would produce, eliminating the need for manual labor
3Measurement precision
If multiple modalities are integrated, then summarization accuracy is improved, but system complexity increases
Solution Approach 1:
The complex task of multi-modal summarization is segmented into independent processing modules: audio event detection, video frame analysis, chat message processing, and a final integration stage. Each module handles one modality separately before their results are combined, making the overall complex system manageable through modular architecture
Solution Approach 2:
The system introduces intermediate representations and feature vectors that serve as mediators between different modalities. Audio features, video features, and text features are converted into a common representation space where they can be compared and integrated, simplifying the complexity of combining fundamentally different data types
Data Source
AI summary
Video and audio from a computer simulation are processed by a machine learning engine to identify candidate segments of the simulation for use in a video summary of the simulation. Text input is then used to reinforce whether a candidate segment should be included in the video summary.