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

Omni-modal dialogue models can understand multimodal inputs and synthesize spoken replies, but a spoken answer still leaves the agent visually absent. We introduce 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 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 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.