Waver: Wave Your Way to Lifelike Video Generation
Authors: name: Yifu Zhang; name: Hao Yang; name: Yuqi Zhang; name: Yifei Hu; name: Fengda Zhu; name: Chuang Lin; name: Xiaofeng Mei; name: Yi Jiang; name: Zehuan Yuan; name: Bingyue Peng
Published: 2025-08-21 17:56:10

Abstract: We present Waver, a high-performance foundation model for unified image and
video generation. Waver can directly generate videos with durations ranging
from 5 to 10 seconds at a native resolution of 720p, which are subsequently
upscaled to 1080p. The model simultaneously supports text-to-video (T2V),
image-to-video (I2V), and text-to-image (T2I) generation within a single,
integrated framework. We introduce a Hybrid Stream DiT architecture to enhance
modality alignment and accelerate training convergence. To ensure training data
quality, we establish a comprehensive data curation pipeline and manually
annotate and train an MLLM-based video quality model to filter for the
highest-quality samples. Furthermore, we provide detailed training and
inference recipes to facilitate the generation of high-quality videos. Building
on these contributions, Waver excels at capturing complex motion, achieving
superior motion amplitude and temporal consistency in video synthesis. Notably,
it ranks among the Top 3 on both the T2V and I2V leaderboards at Artificial
Analysis (data as of 2025-07-30 10:00 GMT+8), consistently outperforming
existing open-source models and matching or surpassing state-of-the-art
commercial solutions. We hope this technical report will help the community
more efficiently train high-quality video generation models and accelerate
progress in video generation technologies. Official page:
https://github.com/FoundationVision/Waver.