We present StereoFoley, a video-to-audio generation framework that produces semantically aligned, temporally synchronized, and spatially accurate stereo sound at 48 kHz. While recent generative video-to-audio models achieve strong semantic and temporal fidelity, they largely remain limited to mono or fail to deliver object-aware stereo imaging, constrained by the lack of professionally mixed, spatially accurate video-to-audio datasets. First, we develop a base model that generates stereo audio from video, achieving performance on par with state-of-the-art V2A models in both semantic accuracy and synchronization. Next, to overcome dataset limitations, we introduce a synthetic data generation pipeline that combines video analysis, object tracking, and audio synthesis with dynamic panning and distance-based loudness controls, enabling spatially accurate object-aware sound. Finally, we fine-tune the base model on this synthetic dataset, yielding clear object-audio correspondence. Since no established metrics exist, we introduce a stereo object-awareness metric and report it alongside a human listening study; the two evaluations exhibit consistent trends. This work establishes the first end-to-end framework for stereo object-aware video-to-audio generation, addressing a critical gap in the field.

翻译：我们提出StereoFoley，一种视频到音频生成框架，能够以48kHz采样率生成语义对齐、时间同步且空间精确的立体声。尽管近期生成的视频到音频模型在语义和时间保真度上表现优异，但大多局限于单声道或无法实现物体感知的立体声成像，这受限于缺乏专业混音、空间精确的视频到音频数据集。首先，我们开发了一个从视频生成立体声音频的基础模型，其在语义准确性和同步性方面均达到与最先进的V2A模型相当的性能。其次，为克服数据集局限性，我们引入了一种合成数据生成流水线，该流水线结合了视频分析、目标跟踪、音频合成以及动态声像与基于距离的响度控制，从而支持空间精确的物体感知声音。最后，我们在此合成数据集上微调基础模型，实现了清晰的物体-音频对应关系。由于缺乏既定评估指标，我们提出了一种立体物体感知度量，并结合人类聆听研究进行报告；两项评估呈现出一致趋势。本研究建立了首个面向立体声物体感知的视频到音频生成的端到端框架，填补了该领域的关键空白。