TAVGBench: Benchmarking Text to Audible-Video Generation
arxiv(2024)
摘要
The Text to Audible-Video Generation (TAVG) task involves generating videos
with accompanying audio based on text descriptions. Achieving this requires
skillful alignment of both audio and video elements. To support research in
this field, we have developed a comprehensive Text to Audible-Video Generation
Benchmark (TAVGBench), which contains over 1.7 million clips with a total
duration of 11.8 thousand hours. We propose an automatic annotation pipeline to
ensure each audible video has detailed descriptions for both its audio and
video contents. We also introduce the Audio-Visual Harmoni score (AVHScore) to
provide a quantitative measure of the alignment between the generated audio and
video modalities. Additionally, we present a baseline model for TAVG called
TAVDiffusion, which uses a two-stream latent diffusion model to provide a
fundamental starting point for further research in this area. We achieve the
alignment of audio and video by employing cross-attention and contrastive
learning. Through extensive experiments and evaluations on TAVGBench, we
demonstrate the effectiveness of our proposed model under both conventional
metrics and our proposed metrics.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要