Game Video Summarization Using Multimodal Metadata Alignment
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Solution Overview
Problem
Generating an effective summary of computer simulation videos automatically is difficult, and manual summarization is time-consuming.
Innovation Solution
A system utilizing a machine learning engine to process audio-video data, aligning metadata with audio and video modalities, and generating a concise video summary that includes metadata perceptible in the video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual summarization is used to create effective video summaries, then the quality of summary is improved, but the time consumption increases
Solution Approach 1:
The patent segments the video into multiple clips based on metadata analysis, audio features, and video features. The ML engine processes different segments independently to identify important portions, allowing automated summarization to achieve quality comparable to manual methods while significantly reducing time consumption through parallel processing of segmented content.
Solution Approach 2:
The patent introduces metadata as an intermediary element that bridges automated processing and human-perceptible information. Metadata including game event data, emotion, audio, and video features are processed by the ML engine to automatically identify and select important video segments, enabling automated summarization to produce high-quality results without manual intervention.
2Productivity
If automated summarization is used to reduce time consumption, then the processing speed is improved, but the summary quality deteriorates
Solution Approach 1:
The patent employs dynamic processing where the ML engine continuously analyzes metadata, audio, and video features to adaptively select video segments. The system dynamically adjusts which clips to include based on real-time analysis of multiple feature types, allowing automated processing to maintain high summary quality while achieving fast processing speeds through efficient feature-based selection.
Solution Approach 2:
The patent changes multiple parameters simultaneously including metadata types (game event data, emotion, audio, video features), audio features (pitch, power, acoustic events), and video features (scene changes, object detection). By analyzing and weighting multiple parameters, the ML engine enables automated summarization to produce high-quality summaries that capture important moments without manual intervention.
3Adaptability or versatility
If multiple metadata types are integrated into the video summary, then the viewer engagement is improved, but the system complexity increases
Solution Approach 1:
The patent implements a universal ML engine that processes multiple types of metadata (game event data, emotion, audio, video features) through a single integrated system. The engine universally handles diverse input types and produces unified video summaries with overlaid metadata, enabling enhanced viewer engagement while managing system complexity through consolidated processing architecture.
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. Metadata can be added to the summary showing game summary information.