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arXiv · 2608.10720

Ex-Omni-2D: Expressive Omni-Modal Dialogue Models with Native Visual Presence

Haoyu Zhang·Zhipeng Li·Xiaoying Tang·Tianshu Yu·Yiwen Guo
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Abstract

Omni-modal dialogue models can understand multimodal inputs and synthesize spoken replies, but a spoken answer still leaves the agent visually absent. We introduce \textbf{Ex-Omni-2D}, a framework that answers a multimodal query with coordinated text, personalized speech, and reference-conditioned video. The dialogue model first writes a structured \textit{Visual Thought Plan} (VTP) for scene, emotion, and motion, then generates the response text and multi-codebook speech units. These speech units are decoded into audio and aligned with video frames, giving the speech and avatar modules a common timing signal while allowing them to learn from different data sources. The video module is trained as a full-sequence Teacher conditioned on reference appearance, VTP semantics, and frame-aligned speech units. We further explore to distill it into a few-step block-causal \emph{Streaming Student}; its Prefix Streaming mechanism carries the previous clean latent into the next chunk and is analyzed as a partial mitigation for late-chunk subject drift. At $400\times720$/$720\times400$, the four-step four-GPU Student provides incremental output with lower startup latency than the full-sequence Teacher.

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Haoyu Zhang, Zhipeng Li, Xiaoying Tang, Tianshu Yu, Yiwen Guo. 2026-09-03. Ex-Omni-2D: Expressive Omni-Modal Dialogue Models with Native Visual Presence. https://arxiv.org/abs/2608.10720

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