TAVGBench: Benchmarking Text to Audible-Video Generation
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arXiv
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| Format: | Preprint |
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2024
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| author | Mao, Yuxin Shen, Xuyang Zhang, Jing Qin, Zhen Zhou, Jinxing Xiang, Mochu Zhong, Yiran Dai, Yuchao |
| author_facet | Mao, Yuxin Shen, Xuyang Zhang, Jing Qin, Zhen Zhou, Jinxing Xiang, Mochu Zhong, Yiran Dai, Yuchao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_14381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TAVGBench: Benchmarking Text to Audible-Video Generation Mao, Yuxin Shen, Xuyang Zhang, Jing Qin, Zhen Zhou, Jinxing Xiang, Mochu Zhong, Yiran Dai, Yuchao Computer Vision and Pattern Recognition Multimedia 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. |
| title | TAVGBench: Benchmarking Text to Audible-Video Generation |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2404.14381 |