Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model
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.
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SII-GAIR
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Sand. ai
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Ethan Chern
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Hansi Teng
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Hanwen Sun
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Hao Wang
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Hong Pan
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Hongyu Jia
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Jiadi Su
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Jin Li
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Junjie Yu
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Lijie Liu
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Lingzhi Li
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Lyumanshan Ye
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Min Hu
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Qiangang Wang
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Quanwei Qi
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Steffi Chern
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Tao Bu
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Taoran Wang
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Teren Xu
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Tianning Zhang
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Tiantian Mi
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Weixian Xu
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Wenqiang Zhang
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Wentai Zhang
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Xianping Yi
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Xiaojie Cai
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Xiaoyang Kang
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Yan Ma
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Yixiu Liu
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Yunbo Zhang
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Yunpeng Huang
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Yutong Lin
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Zewei Tao
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Zhaoliang Liu
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Zheng Zhang
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Zhiyao Cen
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Zhixuan Yu
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Zhongshu Wang
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Zhulin Hu
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Zijin Zhou
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Zinan Guo
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Yue Cao
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Pengfei Liu
Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model
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.
daVinci-MagiHuman is an open-source audio-video generative model that synchronizes text, video, and audio through a single-stream Transformer architecture, achieving high-quality human-centric content generation with efficient inference capabilities.
audio-video generative foundation model single-stream Transformer self-attention multilingual spoken generation model distillation latent-space super-resolution Turbo VAE decoder visual quality text alignment speech intelligibility