Neural Radiance Field Audio-Visual Synthesis via Cross-Model Bridge
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Solution Overview
Problem
Conventional methods for synthesizing audio-visual scenes in real-world environments face challenges due to the imbalance between visual and audio data, lack of prior knowledge for audio synthesis, and reliance on ground-truth acoustic labels, which limits their applicability and effectiveness.
Innovation Solution
The proposed audio-visual scene synthesis system incorporates an acoustic-aware audio generation module that integrates prior knowledge of audio propagation into Neural Radiance Fields (NeRF), uses a coordinate transformation mechanism to express viewing direction relative to the sound source, and employs binaural audio augmentation to enhance acoustic supervision, enabling the generation of consistent audio-visual scenes at novel camera trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use ground-truth acoustic labels for audio synthesis, then audio synthesis accuracy is improved, but data requirements and system complexity increase
Solution Approach 1:
The system uses visual information from the scene to automatically generate audio characteristics without requiring external acoustic labels. The neural radiance field synthesizes audio-visual scenes where the visual data itself provides the guidance for audio synthesis, making the system self-sufficient and eliminating the need for separate acoustic datasets.
Solution Approach 2:
The patent introduces an acoustic-aware audio generation module that acts as an intermediary between visual neural radiance fields and audio synthesis. This module translates visual scene understanding into audio characteristics, bridging the gap between visual and audio modalities without requiring direct acoustic measurements.
2Manufacturing precision
If conventional methods balance visual and audio data, then synthesis quality is improved, but training data requirements increase
Solution Approach 1:
The patent merges visual and audio synthesis into a unified neural radiance field framework. Instead of treating visual and audio data separately, the system combines them into a single joint representation that learns from visual inputs to generate corresponding audio, reducing the need for separate audio training datasets.
Solution Approach 2:
The visual neural radiance field serves multiple functions: it simultaneously represents visual scene geometry, appearance, and acoustic properties. This multi-functional approach allows the same visual data to drive both visual rendering and audio synthesis, eliminating the need for dedicated audio training data.
3Measurement precision
If the system models three-dimensional visual environment, then spatial audio information is improved, but computational complexity increases
Solution Approach 1:
The system pre-processes visual data during the neural radiance field training phase to extract and encode spatial and acoustic scene characteristics. By performing this analysis beforehand, the system creates a compact representation that can be efficiently queried during audio synthesis without requiring complex real-time computations.
Solution Approach 2:
The patent transforms the problem from direct audio synthesis to parameter-based control, where the neural radiance field learns to predict acoustic parameters (such as spatial position, distance, and environmental characteristics) that then guide audio generation. This parameter-based approach reduces computational complexity compared to direct waveform synthesis.
Data Source
AI summary
An audio-visual scene synthesis system may include a visual neural network, a cross-model bridge, and an audio neural network. Parameters of the audio neural network may be generated by the cross-model bridge based on analysis of a 3-dimensional visual environment modeled by the visual neural network. A coordinate transformation module may apply a transformation to an input camera direction to synthesize a new camera direction. The audio neural network may utilize the new camera direction and the parameters of the audio neural network to synthesize a multi-channel audio signal corresponding to the new camera direction. Various other devices, systems, and methods are also disclosed.