UniVerse-1: Unified Audio-Video Generation via Stitching of Experts
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909775522955264 |
|---|---|
| author | Wang, Duomin Zuo, Wei Li, Aojie Chen, Ling-Hao Liao, Xinyao Zhou, Deyu Yin, Zixin Dai, Xili Jiang, Daxin Yu, Gang |
| author_facet | Wang, Duomin Zuo, Wei Li, Aojie Chen, Ling-Hao Liao, Xinyao Zhou, Deyu Yin, Zixin Dai, Xili Jiang, Daxin Yu, Gang |
| contents | We introduce UniVerse-1, a unified, Veo-3-like model capable of simultaneously generating coordinated audio and video. To enhance training efficiency, we bypass training from scratch and instead employ a stitching of experts (SoE) technique. This approach deeply fuses the corresponding blocks of pre-trained video and music generation experts models, thereby fully leveraging their foundational capabilities. To ensure accurate annotations and temporal alignment for both ambient sounds and speech with video content, we developed an online annotation pipeline that processes the required training data and generates labels during training process. This strategy circumvents the performance degradation often caused by misalignment text-based annotations. Through the synergy of these techniques, our model, after being finetuned on approximately 7,600 hours of audio-video data, produces results with well-coordinated audio-visuals for ambient sounds generation and strong alignment for speech generation. To systematically evaluate our proposed method, we introduce Verse-Bench, a new benchmark dataset. In an effort to advance research in audio-video generation and to close the performance gap with state-of-the-art models such as Veo3, we make our model and code publicly available. We hope this contribution will benefit the broader research community. Project page: https://dorniwang.github.io/UniVerse-1/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniVerse-1: Unified Audio-Video Generation via Stitching of Experts Wang, Duomin Zuo, Wei Li, Aojie Chen, Ling-Hao Liao, Xinyao Zhou, Deyu Yin, Zixin Dai, Xili Jiang, Daxin Yu, Gang Computer Vision and Pattern Recognition We introduce UniVerse-1, a unified, Veo-3-like model capable of simultaneously generating coordinated audio and video. To enhance training efficiency, we bypass training from scratch and instead employ a stitching of experts (SoE) technique. This approach deeply fuses the corresponding blocks of pre-trained video and music generation experts models, thereby fully leveraging their foundational capabilities. To ensure accurate annotations and temporal alignment for both ambient sounds and speech with video content, we developed an online annotation pipeline that processes the required training data and generates labels during training process. This strategy circumvents the performance degradation often caused by misalignment text-based annotations. Through the synergy of these techniques, our model, after being finetuned on approximately 7,600 hours of audio-video data, produces results with well-coordinated audio-visuals for ambient sounds generation and strong alignment for speech generation. To systematically evaluate our proposed method, we introduce Verse-Bench, a new benchmark dataset. In an effort to advance research in audio-video generation and to close the performance gap with state-of-the-art models such as Veo3, we make our model and code publicly available. We hope this contribution will benefit the broader research community. Project page: https://dorniwang.github.io/UniVerse-1/. |
| title | UniVerse-1: Unified Audio-Video Generation via Stitching of Experts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.06155 |