Papers › Read, Watch and Scream! Sound Generation from Text and Video
Read, Watch and Scream! Sound Generation from Text and Video
Yujin Jeong, Yunji Kim, Sanghyuk Chun, Jiyoung Lee
Despite the impressive progress of multimodal generative models, video-to-audio generation still suffers from limited performance and limits the flexibility to prioritize sound synthesis for specific objects within the scene. Conversely, text-to-audio generation methods generate high-quality audio but pose challenges in ensuring comprehensive scene depiction and time-varying control. To tackle these challenges, we propose a novel video-and-text-to-audio generation method, called \ours, where video serves as a conditional control for a text-to-audio generation model. Especially, our method estimates the structural information of sound (namely, energy) from the video while receiving key content cues from a user prompt. We employ a well-performing text-to-audio model to consolidate the video control, which is much more efficient for training multimodal diffusion models with massive triplet-paired (audio-video-text) data. In addition, by separating the generative components of audio, it becomes a more flexible system that allows users to freely adjust the energy, surrounding environment, and primary sound source according to their preferences. Experimental results demonstrate that our method shows superiority in terms of quality, controllability, and training efficiency. Code and demo are available at https://naver-ai.github.io/rewas.
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Code
Syntology Ran 18 of 22 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 2 ran · violated contract; 5 ran · our draft was wrong; 1 ran · fixture could not drive it; 10 ran with no contract checked.
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video-to-Sound Generation | VGG-Sound | ReWas | FAD | 2.16 | #7 of 8 | Archive leaderboard | report |
| Video-to-Sound Generation | VGG-Sound | ReWas | FD | 15.24 | #7 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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