MIDAS: Multimodal Interactive Digital-human Synthesis via Real-time
  Autoregressive Video Generation
Authors: name: Ming Chen, name: Liyuan Cui, name: Wenyuan Zhang, name: Haoxian Zhang, name: Yan Zhou, name: Xiaohan Li, name: Xiaoqiang Liu, name: Pengfei Wan

Recently, interactive digital human video generation has attracted widespread
attention and achieved remarkable progress. However, building such a practical
system that can interact with diverse input signals in real time remains
challenging to existing methods, which often struggle with high latency, heavy
computational cost, and limited controllability. In this work, we introduce an
autoregressive video generation framework that enables interactive multimodal
control and low-latency extrapolation in a streaming manner. With minimal
modifications to a standard large language model (LLM), our framework accepts
multimodal condition encodings including audio, pose, and text, and outputs
spatially and semantically coherent representations to guide the denoising
process of a diffusion head. To support this, we construct a large-scale
dialogue dataset of approximately 20,000 hours from multiple sources, providing
rich conversational scenarios for training. We further introduce a deep
compression autoencoder with up to 64times reduction ratio, which
effectively alleviates the long-horizon inference burden of the autoregressive
model. Extensive experiments on duplex conversation, multilingual human
synthesis, and interactive world model highlight the advantages of our approach
in low latency, high efficiency, and fine-grained multimodal controllability.