SyncFlow: Temporally Aligned Joint Audio-Video Generation from Text

Haohe Liu, Gael Le Lan, Xinhao Mei, Zhaoheng Ni, Anurag Kumar, Varun K. Nagaraja, Wenwu Wang, Mark D Plumbley, Yangyang Shi, Vikas Chandra

Keywords: Text-to-Audio-Video-Joint Generation, Flow matching, Diffusion Transformer

TL;DR: SyncFlow generates synchronized video and audio from text using a dual-diffusion-transformer, with efficient multi-stage training strategy, and strong zero-shot capabilities on V2A and generalization new video resolution.

Abstract: Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, producing both modalities simultaneously still typically relies on either a cascaded process or multi-modal contrastive encoders. These approaches, however, often lead to suboptimal results due to inherent information losses during inference and conditioning. In this paper, we introduce SyncFlow, a system that is capable of simultaneously generating temporally synchronized audio and video from text. The core of SyncFlow is the proposed dual-diffusion-transformer (d-DiT) architecture, which enables joint video and audio modelling with proper information fusion. To efficiently manage the computational cost of joint audio and video modelling, SyncFlow utilizes a multi-stage training strategy that separates video and audio learning before joint fine-tuning. Our empirical evaluations demonstrate that SyncFlow produces audio and video outputs that are more correlated than baseline methods with significantly enhanced audio quality and audio-visual correspondence. Moreover, we demonstrate strong zero-shot capabilities of SyncFlow, including zero-shot video-to-audio generation and adaptation to novel video resolutions without further training.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 7555