FastInteract: Few-Step Human-Object Interaction Video Generation via Attention Perturbation

Few-step distillation accelerates video diffusion models, but preserving coherent human–object interaction (HOI) while maintaining reference product consistency remains challenging. We introduce **A**ttention **P**erturbation (**AP**) to provide interaction guidance for distribution matching distillation (DMD). AP compares teacher predictions by selectively enabling or blocking target-video queries' access to conditioning keys and values, with inputs and all other attention connections fixed. An auxiliary HOI representation predicted by the student supplies an interaction-enhancement response, and a second contrast using product-reference attention provides a preservation direction. We coordinate these responses through a one-sided projection that removes the component of the enhancement response opposing the preservation direction before augmenting the DMD target. We further curate a multimodal HOI video dataset **_InteractVerse_** from product-oriented livestreams, with aligned person and product references, text, audio, motion, and spatial annotations. Experiments and ablations demonstrate that our method improves interaction plausibility and reference consistency over vanilla DMD.