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arXiv · 2608.24579

Visually-Guided Spatial Audio Generation for $360^\circ$ In-the-Wild Speech Scenes

Qingyu Luo·Peng Zhang·Wenwu Wang·Philip J. B. Jackson
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Abstract

Spatial audio is a key component of immersive $360^\circ$ media, yet high-quality spatial capture remains limited in real-world speech-dominant scenes. We study visually guided First-Order Ambisonics (FOA) speech spatialization in the wild: given aligned $360^\circ$ video and an omnidirectional audio track, we recover the missing directional FOA components. To support this task, we introduce YT-SPEECH, a speech-oriented $360^\circ$ video-FOA dataset curated from YouTube. We propose a two-stage Localizer-Renderer framework, where an audio-visual segmentation backbone provides frame-wise spatial heatmaps and a conditional complex-domain U-Net reconstructs directional FOA signals from the omnidirectional channel. A confidence-based gating strategy stabilizes conditioning under ambiguous acoustic conditions. Experiments show improved reconstruction fidelity, spatial accuracy, and perceptual speech quality relative to ablated variants and prior approaches.

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Qingyu Luo, Peng Zhang, Wenwu Wang, Philip J. B. Jackson. 2026-08-25. Visually-Guided Spatial Audio Generation for $360^\circ$ In-the-Wild Speech Scenes. https://arxiv.org/abs/2608.24579

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