DyaChain: Strategizing Concurrent, Holistic and Dyadic Motion Generation via Progressive Interaction Chain

Generating conversational motion is fundamental to creating lifelike virtual humans for immersive communication and human–computer interaction. While existing methods enable reactive individual movement synthesis, they overlook the **concurrent** and **holistic** behaviors of both speakers in an interactive manner. Recent datasets also rarely provide aligned annotations at both the interlocutor and interaction levels. To address these limitations, we introduce **HOWDY**, a dyadic conversation dataset that augments synchronized speech and holistic 3D motion from both participants with fine-grained body-part motion descriptions and hierarchical interaction annotations. Additionally, we propose **DyaChain**, a holistic motion generation framework to concurrently model dyadic speakers by strategizing a **Progressive Interaction Chain** approach. To ensure spatiotemporal coherence between the two concurrently evolving motion streams while preserving asynchronous action with response dynamics, the chain progresses through three levels: conversational states, dyadic relations, and motion events. Here, we first derive temporally aligned conversational states to delineate speaking and response periods, and then reason dyadic relations between interlocutors. Conditioned on these cues, we design a Temporal-Relational Interactor **TRI** module to capture cross-individual body-part dependencies and asynchronous responses using causal counterpart attention and learnable response delays. Moreover, we retrieve static posture descriptions from the HOWDY and then infer part-aligned dynamic motion events, motivating a newly proposed Part-Aligned Semantic Routing **PSR** module. PSR decomposes holistic motion into semantically coherent body regions and injects static and dynamic motion semantics into the corresponding motion representations to guide region-specific dynamics. Extensive experiments demonstrate that **DyaChain** outperforms SOTA methods for dyadic motion generation.