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.
Figures & tables
Figure 1: Examples of videos generated by daVinci-MagiHuman
Figure 2: Overall architecture. (a) The base generator takes text tokens, a reference image latent, and noisy video and audio tokens as input, and jointly denoises the video and audio tokens with a single-stream Transformer. All modalities are processed within a unified token sequence using self-attention only, without separate cross-attention or fusion modules. A latent-space super-resolution stage can be further applied to refine the generated video at higher output resolutions. (b) The single-stream Transformer adopts a sandwich architecture layout: the first and last 4 layers use modality-specific projections and normalization parameters, while the middle 32 layers share the main Transformer parameters across modalities. Each block uses per-head gating in attention, and the model contains no explicit timestep embedding.
Video Quality Score
Audio Quality Score
Model
Visual Quality ↑
Text Alignment ↑
Physical Consistency ↑
WER ↓
OVI 1.1
4.73
4.10
4.41
40.45%
LTX 2.3
4.76
4.12
4.56
19.23%
daVinci-MagiHuman
4.80
4.18
4.52
14.60%
Table 1: Quantitative Analysis of Ovi-1.1, LTX-2.3, and daVinci-MagiHuman.
Resolution
Base
SR
Decode
Total
256p
1.6
–
0.4
2.0
540p
1.6
5.1
1.3
8.0
1080p
1.6
31.0
5.8
38.4
Table 2: Time breakdown (in seconds) for generating a 5-second video at different resolutions. All results are measured on a single H100 GPU. The base stage uses the distilled model, and all decode times are measured with the Turbo VAE decoder.
Figure 3: Human evaluation results. Pairwise preference rates of daVinci-MagiHuman against two open-source audio-video generation models, aggregated over 10 raters and 2,000 comparisons.