InterDyad: Interactive Dyadic Speech-to-Video Generation by Querying Intermediate Visual Guidance

• URL: http://arxiv.org/abs/2603.23132v1
• Date: Tue, 24 Mar 2026 12:27:52 GMT
• Title: InterDyad: Interactive Dyadic Speech-to-Video Generation by Querying Intermediate Visual Guidance
• Abstract summary: 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.
• Score: 20.740979380270126
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Despite progress in speech-to-video synthesis, existing methods often struggle to capture cross-individual dependencies and provide fine-grained control over reactive behaviors in dyadic settings. To address these challenges, we propose InterDyad, a framework that enables naturalistic interactive dynamics synthesis via querying structural motion guidance. Specifically, we first design an Interactivity Injector that achieves video reenactment based on identity-agnostic motion priors extracted from reference videos. Building upon this, we introduce a MetaQuery-based modality alignment mechanism to bridge the gap between conversational audio and these motion priors. 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. To further improve lip-sync quality under extreme head poses, we propose Role-aware Dyadic Gaussian Guidance (RoDG) for enhanced lip-synchronization and spatial consistency. Finally, we introduce a dedicated evaluation suite with novelly designed metrics to quantify dyadic interaction. Comprehensive experiments demonstrate that InterDyad significantly outperforms state-of-the-art methods in producing natural and contextually grounded two-person interactions. Please refer to our project page for demo videos: https://interdyad.github.io/.

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