DyaPlex: Full-Duplex Speech-Motion Model for Dyadic Interaction
- URL: http://arxiv.org/abs/2606.03874v1
- Date: Tue, 02 Jun 2026 16:42:56 GMT
- Title: DyaPlex: Full-Duplex Speech-Motion Model for Dyadic Interaction
- Abstract summary: To capture continuous reciprocal human communication, this model simultaneously generate and generate both speech and speech physical motion in a streaming fashion.<n>By introducing a unified dyadic token interleaving mechanism and guiding cross-attention, our model effectively aligns autoremotion with latent rich speech features.<n>Our model effectively captures cross-attention dependencies and establishes new state-of-the-art performance across both monadic and dyadic human interaction benchmarks.
- Score: 41.29236046858134
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present DyaPlex, a streaming, full-duplex speech-and-motion model designed for dyadic interaction. To capture the continuous and reciprocal nature of human communication, this full-duplex capability empowers the agent to simultaneously perceive and generate both speech and physical motion in a streaming fashion. At its core, our method leverages the strong priors of a foundational full-duplex speech model and integrates a novel motion pathway, thereby achieving fully synchronized multi-modal interaction. Specifically, we design a dual-tower Transformer architecture that preserves the zero-shot conversational reasoning of a frozen base speech model while constructing a deeply coupled, streaming motion pathway. By introducing a unified dyadic token interleaving mechanism and guiding cross-attention via a time-aligned speech-motion RoPE, our model effectively aligns autoregressive motions with rich latent speech features. Trained on the 4,000-hour Seamless Interaction dataset, our model effectively captures cross-speaker dependencies and establishes new state-of-the-art performance across both monadic and dyadic human interaction benchmarks.
Related papers
- Beyond Monologue: Interactive Talking-Listening Avatar Generation with Conversational Audio Context-Aware Kernels [26.18760496276335]
We develop an interactive virtual agent capable of simultaneously processing dual-stream audio inputs for both talking and listening.<n>Our approach successfully fuses strong temporal alignment with contextual semantics, setting a new state-of-the-art for generating highly natural and responsive interactive digital humans.
arXiv Detail & Related papers (2026-04-11T22:34:21Z) - InterDyad: Interactive Dyadic Speech-to-Video Generation by Querying Intermediate Visual Guidance [20.740979380270126]
We propose InterDyad, a synthesis framework for interactive dyadic dynamics.<n>We first design an Interactivity that achieves video reenactment based on identity-agnostic motion priors extracted from reference videos.<n>By leveraging a Multimodal Large Language Model (MLLM), our framework is able to distill linguistic intent from audio to dictate the precise timing and appropriateness of reactions.<n> Comprehensive experiments demonstrate that InterDyad significantly outperforms state-of-the-art methods in producing natural and contextually grounded two-person interactions.
arXiv Detail & Related papers (2026-03-24T12:27:52Z) - Talking Together: Synthesizing Co-Located 3D Conversations from Audio [21.481529577056904]
We tackle the challenging task of generating complete 3D facial animations for two interacting, co-located participants from a mixed audio stream.<n>Our work is the first to explicitly model the dynamic 3D spatial relationship that is crucial for realistic in-person dialogues.<n>Our method generates fluid, controllable, and spatially aware dyadic animations suitable for immersive applications in VR and telepresence.
arXiv Detail & Related papers (2026-03-09T17:46:52Z) - MIBURI: Towards Expressive Interactive Gesture Synthesis [62.45332399212876]
Embodied Conversational Agents (ECAs) aim to emulate human face-to-face interaction through speech, gestures, and facial expressions.<n>Existing solutions for ECAs produce rigid, low-diversity motions that are unsuitable for human-like interaction.<n>We present MIBURI, the first online, causal framework for generating expressive full-body gestures and facial expressions synchronized with real-time spoken dialogue.
arXiv Detail & Related papers (2026-03-03T18:59:51Z) - U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation [48.6868174403074]
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.
arXiv Detail & Related papers (2026-02-27T07:07:02Z) - Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models [80.28579390566298]
We introduce Interact2Ar, a text-conditioned autoregressive diffusion model for generating full-body, human-human interactions.<n>Hand kinematics are incorporated through dedicated parallel branches, enabling high-fidelity full-body generation.<n>Our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios.
arXiv Detail & Related papers (2025-12-22T18:59:50Z) - End-to-end Listen, Look, Speak and Act [22.047534228540783]
ELLSA represents a step toward more natural and general interactive intelligence, contributing to the broader pursuit of artificial intelligence.<n>At its core is a novel SA-MoE (Attention Mixture-of-Experts) that routes each modality to specialized experts fuses them through a unified attention backbone.
arXiv Detail & Related papers (2025-10-19T08:45:46Z) - FLEXI: Benchmarking Full-duplex Human-LLM Speech Interaction [49.83226596963294]
Speech-computer human interaction enables real-time spoken dialogue systems.<n>Modelling and benchmarking these models remains a fundamental challenge.<n>We introduce FLEXI, the first benchmark for full-human spoken interaction.
arXiv Detail & Related papers (2025-09-26T11:57:42Z) - AsynFusion: Towards Asynchronous Latent Consistency Models for Decoupled Whole-Body Audio-Driven Avatars [71.90109867684025]
Whole-body audio-driven avatar pose and expression generation is a critical task for creating lifelike digital humans.<n>We propose AsynFusion, a novel framework that leverages diffusion transformers to achieve cohesive expression and gesture synthesis.<n>AsynFusion achieves state-of-the-art performance in generating real-time, synchronized whole-body animations.
arXiv Detail & Related papers (2025-05-21T03:28:53Z) - It Takes Two: Real-time Co-Speech Two-person's Interaction Generation via Reactive Auto-regressive Diffusion Model [34.94330722832987]
We introduce an audio-driven, auto-regressive system designed to synthesize dynamic movements for two characters during a conversation.<n>To the best of our knowledge, this is the first system capable of generating interactive full-body motions for two characters from speech in an online manner.
arXiv Detail & Related papers (2024-12-03T12:31:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.