U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation

• URL: http://arxiv.org/abs/2602.23739v1
• Date: Fri, 27 Feb 2026 07:07:02 GMT
• Title: U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation
• Authors: Xiang Deng, Feng Gao, Yong Zhang, Youxin Pang, Xu Xiaoming, Zhuoliang Kang, Xiaoming Wei, Yebin Liu,
• Abstract summary: We introduce U-Mind, the first unified system for high-intelligence multimodal dialogue.<n>It supports real-time generation and jointly models language, speech, motion, and video synthesis within a single interactive loop.<n>We show that U-Mind achieves state-of-the-art performance on a range of multimodal interaction tasks.
• Score: 48.6868174403074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Full-stack multimodal interaction in real-time is a central goal in building intelligent embodied agents capable of natural, dynamic communication. However, existing systems are either limited to unimodal generation or suffer from degraded reasoning and poor cross-modal alignment, preventing coherent and perceptually grounded interactions. In this work, we introduce U-Mind, the first unified system for high-intelligence multimodal dialogue that supports real-time generation and jointly models language, speech, motion, and video synthesis within a single interactive loop. At its core, U-Mind implements a Unified Alignment and Reasoning Framework that addresses two key challenges: enhancing cross-modal synchronization via a segment-wise alignment strategy, and preserving reasoning abilities through Rehearsal-Driven Learning. During inference, U-Mind adopts a text-first decoding pipeline that performs internal chain-of-thought planning followed by temporally synchronized generation across modalities. To close the loop, we implement a real-time video rendering framework conditioned on pose and speech, enabling expressive and synchronized visual feedback. Extensive experiments demonstrate that U-Mind achieves state-of-the-art performance on a range of multimodal interaction tasks, including question answering, instruction following, and motion generation, paving the way toward intelligent, immersive conversational agents.

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