Game Animation Audio Cue Placement Using Confidence Scoring
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Solution Overview
Problem
Conventional video game animation engines require significant manual input from audio engineers to place audio cues at appropriate timesteps in animations, consuming substantial time and computing resources.
Innovation Solution
A video game animation engine that utilizes a computer-implemented model to compute confidence scores for audio cue placement based on feature values of interconnected bones in animations, reducing manual input and refining placements using a heuristic algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual audio cue placement is used in conventional video game animation engines, then audio cues can be placed at appropriate timesteps, but significant time and computing resources are consumed
Solution Approach 1:
The system performs preliminary analysis of animation data during the training phase to establish patterns and relationships between animation features and audio cue placements. This pre-computed knowledge is stored and reused during actual audio cue placement, eliminating the need for repeated manual analysis and significantly reducing time consumption while maintaining placement accuracy
Solution Approach 2:
A machine learning model serves as an intermediary between the animation data and audio cue placement decisions. The model automatically analyzes animation features and predicts appropriate audio cue placements without requiring manual intervention, thus resolving the contradiction between accurate placement and time consumption
2Measurement precision
If manual audio cue placement is used in conventional video game animation engines, then audio cues can be placed at appropriate timesteps, but significant computing resources are consumed
Solution Approach 1:
The system performs computationally intensive analysis during an offline training phase to build a machine learning model that captures the relationship between animation features and audio cue placements. During runtime, the pre-trained model makes rapid predictions with minimal computing resources, thus maintaining accuracy while reducing real-time resource consumption
Solution Approach 2:
The system creates a computational model that copies and generalizes the patterns of manual audio cue placement decisions. Once trained on examples of correct placements, the model can automatically reproduce these decisions across new animations without requiring the same level of computing resources as manual placement
3Extent of automation
If a computer-implemented model is used for audio cue placement, then manual input is reduced, but the system complexity increases
Solution Approach 1:
The machine learning model is trained using automatically generated synthetic data from animation simulations, eliminating the need for manual labeling and reducing system complexity. The system serves itself by generating its own training data through simulation, thus achieving high automation without proportionally increasing complexity
Solution Approach 2:
The system transforms the complex task of audio cue placement into a parameter prediction problem where the model learns to predict timestamps based on animation features. This parameter-based approach simplifies the automation process while maintaining effectiveness
Data Source
AI summary
Described herein are technologies relating to insertion of audio cues for a sound into animations of video games. A video game animation engine obtains feature values corresponding to an animation at a timestep and provides the feature values to a computer-implemented model. The computer-implemented model computes a confidence score for the timestep, where the confidence score is indicative of a likelihood that an audio cue for the sound should be inserted in the animation at the timestep. The video game animation engine inserts the audio cue for the sound at the timestep based upon the confidence score.