Real-Time Avatar Animation Audio Separation
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Solution Overview
Problem
Current technologies for real-time avatar animation in karaoke settings lack the ability to seamlessly synchronize avatar movements and expressions with audio inputs, particularly in mixed vocal and non-vocal sound environments, and do not effectively incorporate user instructions for dynamic animation.
Innovation Solution
A deep-neural-network (DNN) architecture that separates audio inputs into vocal and non-vocal components using a wave-u-net backbone, combined with instruction classification models, to generate synchronized avatar animations, including lip-sync, facial expressions, and dance movements, based on both audio features and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio source separation is performed to distinguish vocal and non-vocal sounds, then avatar animation synchronization is improved, but computational complexity increases
Solution Approach 1:
The audio input is segmented into distinct vocal and non-vocal components using a trained source separation model. This segmentation enables the system to process different audio types differently, improving avatar animation synchronization by applying appropriate animation rules to each audio component separately.
Solution Approach 2:
The source separation model is trained in advance on a dataset of labeled audio segments to learn the characteristics of vocal versus non-vocal sounds. This preliminary training action allows the model to perform rapid separation during runtime without requiring complex real-time computation, thus improving synchronization while managing computational complexity.
2Manufacturing precision
If multiple animation parameters are generated from audio features, then avatar expression accuracy is improved, but processing time increases
Solution Approach 1:
The animation generation process is segmented into multiple independent parameter generation steps, each handling a specific aspect of avatar animation (e.g., facial expressions, body movements, lip-sync). This segmentation allows parallel processing of different animation parameters, improving overall expression accuracy while reducing total processing time through concurrent computation.
Solution Approach 2:
The system generates multiple animation parameters beyond what might be minimally required, including detailed facial expressions, body movements, and lip-sync parameters. This excessive action approach ensures high avatar expression accuracy by providing comprehensive animation control, while the use of efficient neural network models keeps processing time acceptable.
3Adaptability or versatility
If user instructions are integrated with audio features for animation control, then interaction quality is improved, but system complexity increases
Solution Approach 1:
User instructions and audio features are merged into a unified animation control framework. The system processes both audio-derived features and user-provided instructions through integrated neural network models, allowing seamless combination of automatic audio-driven animation and user-directed customization. This merging improves interaction quality by enabling users to override or modify automatic animations while maintaining system-wide consistency.
Solution Approach 2:
The animation generation system is designed with universal models that can handle multiple types of inputs (audio features, user instructions, emotion tags) and produce multiple types of outputs (facial expressions, body movements, lip-sync). This multi-functionality improves interaction quality by allowing the same system to serve different karaoke scenarios and user preferences without requiring separate specialized systems.
Data Source
AI summary
In one embodiment, a method includes accessing an audio input that includes a mixture of vocal sounds and non-vocal sounds and separating, by a trained audio source separation model, the audio input into a first audio output representing the vocal sounds and a second audio output representing the non-vocal sounds. The method further includes determining, by one or more trained avatar animation models and by separately encoding the first audio output representing the vocal sounds and the second audio output representing the non-vocal sounds, an avatar animation temporally corresponding to the audio input; and rendering, in real time and temporally coincident with the audio input, the determined avatar animation.