ALIVE: Animate Your World with Lifelike Audio-Video Generation
Abstract
Video generation is rapidly evolving towards unified audio-video generation. In this paper, we present ALIVE, a generation model that adapts a pretrained Text-to-Video (T2V) model to Sora-style audio-video generation and animation. In particular, the model unlocks the Text-to-Video&Audio (T2VA) and Reference-to-Video&Audio (animation) capabilities compared to the T2V foundation models. To support the audio-visual synchronization and reference animation, we augment the popular MMDiT architecture with a joint audio-video branch which includes TA-CrossAttn for temporally-aligned cross-modal fusion and UniTemp-RoPE for precise audio-visual alignment. Meanwhile, a comprehensive data pipeline consisting of audio-video captioning, quality control, etc., is carefully designed to collect high-quality finetuning data. Additionally, we introduce a new benchmark to perform a comprehensive model test and comparison. After continue pretraining and finetuning on million-level high-quality data, ALIVE demonstrates outstanding performance, consistently outperforming open-source models and matching or surpassing state-of-the-art commercial solutions. With detailed recipes and benchmarks, we hope ALIVE helps the community develop audio-video generation models more efficiently. Official page: https://github.com/FoundationVision/Alive.
Keywords
Cite
@article{arxiv.2602.08682,
title = {ALIVE: Animate Your World with Lifelike Audio-Video Generation},
author = {Ying Guo and Qijun Gan and Yifu Zhang and Jinlai Liu and Yifei Hu and Pan Xie and Dongjun Qian and Yu Zhang and Ruiqi Li and Yuqi Zhang and Ruibiao Lu and Xiaofeng Mei and Bo Han and Xiang Yin and Bingyue Peng and Zehuan Yuan},
journal= {arXiv preprint arXiv:2602.08682},
year = {2026}
}
Comments
Technical report for ALIVE. Bytedance ALIVE Team. Homepage: https://foundationvision.github.io/Alive/