University of Rochester (20240267695). NEURAL RADIANCE FIELD SYSTEMS AND METHODS FOR SYNTHESIS OF AUDIO-VISUAL SCENES simplified abstract
Contents
NEURAL RADIANCE FIELD SYSTEMS AND METHODS FOR SYNTHESIS OF AUDIO-VISUAL SCENES
Organization Name
Inventor(s)
Chenliang Xu of Pittsford NY (US)
Susan Liang of Rochester NY (US)
Chao Huang of Rochester NY (US)
Fnu Anurag Kumar of Bothell WA (US)
NEURAL RADIANCE FIELD SYSTEMS AND METHODS FOR SYNTHESIS OF AUDIO-VISUAL SCENES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240267695 titled 'NEURAL RADIANCE FIELD SYSTEMS AND METHODS FOR SYNTHESIS OF AUDIO-VISUAL SCENES
- Simplified Explanation:**
The patent application describes an audio-visual scene synthesis system that uses neural networks to generate audio signals based on a 3-dimensional visual environment.
- Key Features and Innovation:**
- Visual neural network and audio neural network for scene synthesis.
- Cross-model bridge generates audio neural network parameters based on visual analysis.
- Coordinate transformation module for camera direction synthesis.
- Potential Applications:**
This technology could be used in virtual reality, gaming, entertainment, and multimedia content creation.
- Problems Solved:**
This technology addresses the challenge of synthesizing realistic audio-visual scenes in a dynamic environment.
- Benefits:**
- Enhanced audio-visual experience for users.
- Realistic and immersive virtual environments.
- Efficient synthesis of multi-channel audio signals.
- Commercial Applications:**
The technology could be applied in VR gaming, movie production, simulation training, and interactive media experiences.
- Questions about the Technology:**
1. How does the cross-model bridge determine the parameters for the audio neural network? 2. What are the potential limitations of using neural networks for audio-visual scene synthesis?
Original Abstract Submitted
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.
