Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation

• URL: http://arxiv.org/abs/2303.16541v1
• Date: Wed, 29 Mar 2023 09:07:31 GMT
• Title: Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation
• Authors: Jiawei Liu, Weining Wang, Sihan Chen, Xinxin Zhu, Jing Liu
• Abstract summary: Sounding Video Generator (SVG) is a unified framework for generating realistic videos along with audio signals. VQGAN transforms visual frames and audio melspectrograms into discrete tokens. Transformer-based decoder is used to model associations between texts, visual frames, and audio signals.
• Score: 24.403772976932487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded. In this work, we concentrate on a rarely investigated problem of text guided sounding video generation and propose the Sounding Video Generator (SVG), a unified framework for generating realistic videos along with audio signals. Specifically, we present the SVG-VQGAN to transform visual frames and audio melspectrograms into discrete tokens. SVG-VQGAN applies a novel hybrid contrastive learning method to model inter-modal and intra-modal consistency and improve the quantized representations. A cross-modal attention module is employed to extract associated features of visual frames and audio signals for contrastive learning. Then, a Transformer-based decoder is used to model associations between texts, visual frames, and audio signals at token level for auto-regressive sounding video generation. AudioSetCap, a human annotated text-video-audio paired dataset, is produced for training SVG. Experimental results demonstrate the superiority of our method when compared with existing textto-video generation methods as well as audio generation methods on Kinetics and VAS datasets.

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