GAN Synchronization for Volumetric Video Audio-Visual Alignment
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Solution Overview
Problem
Existing volumetric video technologies face challenges in synchronizing sound with visual content in virtual reality (VR) and augmented reality (AR) experiences, as sound properties change with parameters like humidity and distance, requiring appropriate audio-visual alignment that current methods fail to achieve effectively.
Innovation Solution
The use of a generative adversarial network (GAN) model to analyze sound variations, determine necessary changes in volumetric video content, and dynamically modify the video to synchronize sound with objects, allowing for the addition or removal of sounds and their corresponding visual effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sound properties are changed to match environmental parameters like humidity and distance, then the realism of volumetric video is improved, but the synchronization between sound and visual content becomes more difficult to maintain
Solution Approach 1:
The system employs a feedback mechanism where the GAN model continuously analyzes the relationship between visual content and sound properties, adjusting sound parameters based on environmental context while maintaining synchronization. The model learns from the correlation between visual changes and sound variations, automatically correcting deviations to maintain precise alignment.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting sound properties (such as volume, frequency, and spatial position) based on environmental parameters like humidity and distance. The GAN model modifies these sound parameters in real-time to match the visual content while preserving synchronization through learned correlations.
2Manufacturing precision
If a GAN model is used to dynamically modify volumetric video for sound synchronization, then the precision of audio-visual alignment is improved, but the computational complexity and processing time increase
Solution Approach 1:
The GAN model is pre-trained on large datasets of synchronized audio-visual content, learning the complex relationships between visual changes and sound variations beforehand. This preliminary training phase allows the model to perform rapid inference during actual volumetric video processing, reducing real-time computational complexity while maintaining high synchronization precision.
3Loss of information
If sounds are added or removed from volumetric video, then the contextual accuracy is improved, but the difficulty of maintaining synchronization with visual content increases
Solution Approach 1:
The GAN model performs self-service by automatically detecting when sounds should be added or removed based on the visual content analysis. The model independently determines the appropriate sound modifications and executes the synchronization adjustments without requiring manual intervention, thereby maintaining contextual accuracy while managing synchronization complexity autonomously.
Data Source
AI summary
A method is presented including capturing sounds from a plurality of objects within a volumetric video, analyzing the captured sounds to generate context of the captured sounds, determining whether to remove any existing sounds or to add any new sounds resulting in sound variations, dynamically modifying the volumetric video to synchronize the sound variations with the plurality of objects by employing a generative adversarial network (GAN) model, and generating a new volumetric video exhibiting synchronization between the plurality of objects and the sound variations.