Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model
Authors: name: SII-GAIR, name: Sand. ai, name: Ethan Chern, name: Hansi Teng, name: Hanwen Sun, name: Hao Wang, name: Hong Pan, name: Hongyu Jia, name: Jiadi Su, name: Jin Li, name: Junjie Yu, name: Lijie Liu, name: Lingzhi Li, name: Lyumanshan Ye, name: Min Hu, name: Qiangang Wang, name: Quanwei Qi, name: Steffi Chern, name: Tao Bu, name: Taoran Wang, name: Teren Xu, name: Tianning Zhang, name: Tiantian Mi, name: Weixian Xu, name: Wenqiang Zhang, name: Wentai Zhang, name: Xianping Yi, name: Xiaojie Cai, name: Xiaoyang Kang, name: Yan Ma, name: Yixiu Liu, name: Yunbo Zhang, name: Yunpeng Huang, name: Yutong Lin, name: Zewei Tao, name: Zhaoliang Liu, name: Zheng Zhang, name: Zhiyao Cen, name: Zhixuan Yu, name: Zhongshu Wang, name: Zhulin Hu, name: Zijin Zhou, name: Zinan Guo, name: Yue Cao, name: Pengfei Liu

We present daVinci-MagiHuman, an open-source audio-video generative foundation model for human-centric generation. daVinci-MagiHuman jointly generates synchronized video and audio using a single-stream Transformer that processes text, video, and audio within a unified token sequence via self-attention only. This single-stream design avoids the complexity of multi-stream or cross-attention architectures while remaining easy to optimize with standard training and inference infrastructure. The model is particularly strong in human-centric scenarios, producing expressive facial performance, natural speech-expression coordination, realistic body motion, and precise audio-video synchronization. It supports multilingual spoken generation across Chinese (Mandarin and Cantonese), English, Japanese, Korean, German, and French. For efficient inference, we combine the single-stream backbone with model distillation, latent-space super-resolution, and a Turbo VAE decoder, enabling generation of a 5-second 256p video in 2 seconds on a single H100 GPU. In automatic evaluation, daVinci-MagiHuman achieves the highest visual quality and text alignment among leading open models, along with the lowest word error rate (14.60%) for speech intelligibility. In pairwise human evaluation, it achieves win rates of 80.0% against Ovi 1.1 and 60.9% against LTX 2.3 over 2000 comparisons. We open-source the complete model stack, including the base model, the distilled model, the super-resolution model, and the inference codebase.