  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.
