Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.08279 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910998397452288 |
|---|---|
| author | Sundararaman, Aditi Adishesha, Amogh Jaegle, Andrew Bigioi, Dan Song, Hyoung-Kyu Kyl, Jon Mao, Justin Lan, Kevin Komeili, Mojtaba Athar, ShahRukh Babayan, Sheila Beliasau, Stanislau Buchwalter, William |
| author_facet | Sundararaman, Aditi Adishesha, Amogh Jaegle, Andrew Bigioi, Dan Song, Hyoung-Kyu Kyl, Jon Mao, Justin Lan, Kevin Komeili, Mojtaba Athar, ShahRukh Babayan, Sheila Beliasau, Stanislau Buchwalter, William |
| contents | From professional filmmaking to user-generated content, creators and consumers have long recognized that the power of video depends on the harmonious integration of what we hear (the video's audio track) with what we see (the video's image sequence). Current approaches to video generation either ignore sound to focus on general-purpose but silent image sequence generation or address both visual and audio elements but focus on restricted application domains such as re-dubbing. We introduce Mirage, an audio-to-video foundation model that excels at generating realistic, expressive output imagery from scratch given an audio input. When integrated with existing methods for speech synthesis (text-to-speech, or TTS), Mirage results in compelling multimodal video. When trained on audio-video footage of people talking (A-roll) and conditioned on audio containing speech, Mirage generates video of people delivering a believable interpretation of the performance implicit in input audio. Our central technical contribution is a unified method for training self-attention-based audio-to-video generation models, either from scratch or given existing weights. This methodology allows Mirage to retain generality as an approach to audio-to-video generation while producing outputs of superior subjective quality to methods that incorporate audio-specific architectures or loss components specific to people, speech, or details of how images or audio are captured. We encourage readers to watch and listen to the results of Mirage for themselves (see paper and comments for links). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeing Voices: Generating A-Roll Video from Audio with Mirage Sundararaman, Aditi Adishesha, Amogh Jaegle, Andrew Bigioi, Dan Song, Hyoung-Kyu Kyl, Jon Mao, Justin Lan, Kevin Komeili, Mojtaba Athar, ShahRukh Babayan, Sheila Beliasau, Stanislau Buchwalter, William Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning From professional filmmaking to user-generated content, creators and consumers have long recognized that the power of video depends on the harmonious integration of what we hear (the video's audio track) with what we see (the video's image sequence). Current approaches to video generation either ignore sound to focus on general-purpose but silent image sequence generation or address both visual and audio elements but focus on restricted application domains such as re-dubbing. We introduce Mirage, an audio-to-video foundation model that excels at generating realistic, expressive output imagery from scratch given an audio input. When integrated with existing methods for speech synthesis (text-to-speech, or TTS), Mirage results in compelling multimodal video. When trained on audio-video footage of people talking (A-roll) and conditioned on audio containing speech, Mirage generates video of people delivering a believable interpretation of the performance implicit in input audio. Our central technical contribution is a unified method for training self-attention-based audio-to-video generation models, either from scratch or given existing weights. This methodology allows Mirage to retain generality as an approach to audio-to-video generation while producing outputs of superior subjective quality to methods that incorporate audio-specific architectures or loss components specific to people, speech, or details of how images or audio are captured. We encourage readers to watch and listen to the results of Mirage for themselves (see paper and comments for links). |
| title | Seeing Voices: Generating A-Roll Video from Audio with Mirage |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.08279 |