简约致速：面向快速音视频生成的单流基础模型架构

Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model

March 23, 2026

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

cs.AI

摘要

我们推出daVinci-MagiHuman——一款面向人本生成的开源音视频生成基础模型。该模型通过单流Transformer架构，仅依赖自注意力机制在统一标记序列中处理文本、视频和音频数据，实现同步音视频生成。这种单流设计避免了多流或交叉注意力架构的复杂性，同时能利用标准训练推理基础设施轻松优化。该模型在人本生成场景表现卓越，可生成富有表现力的面部表演、自然的语音表情协调、逼真的身体运动以及精准的音画同步效果，支持汉语（普通话与粤语）、英语、日语、韩语、德语、法语等多语言语音生成。为提升推理效率，我们结合模型蒸馏、潜空间超分辨率和Turbo VAE解码器技术，在单张H100 GPU上仅需2秒即可生成5秒时长的256p视频。自动评估显示，daVinci-MagiHuman在主流开源模型中取得最高视觉质量与文本对齐度，语音可懂度词错误率最低（14.60%）。在2000次人工对比评估中，其相对于Ovi 1.1和LTX 2.3的胜率分别达到80.0%和60.9%。我们已开源完整模型栈，包括基础模型、蒸馏模型、超分辨率模型及推理代码库。

English

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.