DynFOA: Generating First-Order Ambisonics with Conditional Diffusion for Dynamic and Acoustically Complex 360-Degree Videos

2026-02-06

AI Summary

Existing methods struggle to generate high-fidelity first-order Ambisonics (FOA) audio in dynamic, acoustically complex 360-degree videos, often neglecting moving sound sources and environmental acoustic effects such as occlusion, reflection, and reverberation. This work proposes a novel framework that integrates 3D Gaussian Splatting with a conditional diffusion model: a video encoder detects dynamic sound sources and reconstructs scene geometry and materials, while an audio encoder models 4D sound source trajectories, enabling real-time synthesis of viewpoint-consistent, high-fidelity FOA. By introducing 3D Gaussian Splatting into spatial audio generation for the first time, the method significantly outperforms existing approaches in spatial accuracy, acoustic fidelity, and distributional alignment, thereby enhancing auditory immersion in virtual reality and other immersive media applications.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Spatial audio is crucial for creating compelling immersive 360-degree video experiences. However, generating realistic spatial audio, such as first-order ambisonics (FOA), from 360-degree videos in complex acoustic scenes remains challenging. Existing methods often overlook the dynamic nature and acoustic complexity of 360-degree scenes, fail to fully account for dynamic sound sources, and neglect complex environmental effects such as occlusion, reflections, and reverberation, which are influenced by scene geometries and materials. We propose DynFOA, a framework based on dynamic acoustic perception and conditional diffusion, for generating high-fidelity FOA from 360-degree videos. DynFOA first performs visual processing via a video encoder, which detects and localizes multiple dynamic sound sources, estimates their depth and semantics, and reconstructs the scene geometry and materials using a 3D Gaussian Splatting. This reconstruction technique accurately models occlusion, reflections, and reverberation based on the geometries and materials of the reconstructed 3D scene and the listener's viewpoint. The audio encoder then captures the spatial motion and temporal 4D sound source trajectories to fine-tune the diffusion-based FOA generator. The fine-tuned FOA generator adjusts spatial cues in real time, ensuring consistent directional fidelity during listener head rotation and complex environmental changes. Extensive evaluations demonstrate that DynFOA consistently outperforms existing methods across metrics such as spatial accuracy, acoustic fidelity, and distribution matching, while also improving the user experience. Therefore, DynFOA provides a robust and scalable approach to rendering realistic dynamic spatial audio for VR and immersive media applications.

Problem

• spatial audio
• first-order ambisonics
• 360-degree video
• acoustic complexity
• dynamic sound sources

Innovation

• conditional diffusion
• first-order ambisonics
• 3D Gaussian Splatting
• dynamic sound sources
• spatial audio rendering