InterCMDM: Block-Causal Diffusion for Autoregressive Human Interaction Generation

Text-conditioned human interaction generation must cap-ture both long-range temporal causality within each individual and tightlycoupled coordination between partners. Existing interaction diffusionmodels typically denoise full sequences using bidirectional attention, whichobscures causality and hinders streaming and long-horizon generation.Autoregressive alternatives enforce causality but often suffer from tem-poral drift, leading to coordination degradation and unstable interactiondynamics over time. We propose InterCMDM, a block-causal latent diffu-sion framework for autoregressive two-person interaction generation. In-terCMDM introduces a Dual-Stream Causal Diffusion Transformer thatmaintains separate causal streams for each person while modeling inter-person dependencies via unified dual-stream attention with multi-taskattention masks. These masks unify interaction modeling within a sin-gle attention mechanism and support diverse coordination behaviors, in-cluding simultaneous actions, reactive responses, leader–follower dynam-ics, and independent motion. By training a single model across thesemask configurations as a form of data augmentation, InterCMDM en-ables controllable interaction generation by simply selecting the desiredattention mask at inference time. Finally, a block-wise diffusion objec-tive enables stable latent rollout over long sequences without repeateddecode–encode cycles. InterCMDM achieves state-of-the-art performanceon InterHuman and Inter-X, improving text–motion alignment, realism,and long-horizon continuity.