StereoFoley: Object-Aware Stereo Audio Generation from Video

2026-04-28 · Source: Apple Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

StereoFoley is a novel video-to-audio generation framework that produces semantically aligned, temporally synchronized, and spatially accurate stereo sound at 48 kHz. Unlike previous models limited to mono audio or lacking object-aware stereo imaging due to dataset constraints, StereoFoley first trains a base model for stereo audio generation, achieving state-of-the-art semantic accuracy and synchronization. To address dataset limitations, the framework introduces a synthetic data generation pipeline that integrates video analysis, object tracking, audio synthesis, dynamic panning, and distance-based loudness controls to enable spatially accurate, object-aware sound. The base model is then fine-tuned on this synthetic dataset, resulting in clear object-audio correspondence. The researchers also developed new stereo object-awareness measures and validated them via a human listening study, establishing StereoFoley as the first end-to-end framework for stereo object-aware video-to-audio generation.

Key takeaway

For Computer Vision Engineers developing video-to-audio systems, StereoFoley demonstrates a critical advancement in generating spatially accurate, object-aware stereo sound. You should consider integrating synthetic data generation pipelines, including object tracking and dynamic panning, to overcome real-world dataset limitations and achieve higher fidelity in spatial audio output. This approach can significantly enhance the realism and immersion of your generated audio experiences.

Key insights

StereoFoley generates object-aware, spatially accurate stereo audio from video using synthetic data to overcome dataset limitations.

Principles

• Synthetic data can bridge real-world dataset gaps.
• Object tracking enhances spatial audio generation.
• Human perception validates novel audio metrics.

Method

StereoFoley trains a base model for stereo audio, then fine-tunes it using a synthetic dataset generated via video analysis, object tracking, audio synthesis, dynamic panning, and distance-based loudness controls.

In practice

• Generate spatially accurate sound for video content.
• Create synthetic datasets for audio-visual tasks.
• Evaluate spatial audio with human listening studies.

Topics

• StereoFoley
• Video-to-Audio Generation
• Object-Aware Audio
• Stereo Sound
• Synthetic Data Generation

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Apple Machine Learning Research.