HOIMask: Towards Generative Masked Modeling for Human Object Interaction Generation

Diffusion-based methods have dominated the HOI genera-tion, as they enable critical contact fusions or signals to guide the dif-fusion process. However, they often result in high artifacts and unstableinteraction quality due to error accumulation during iterative denois-ing. In this work, we propose HOIMask, the first generative maskedframework for modeling HOI motion in discrete space. HOIMask firstencodes both motion sequences and contact-aware signals into discrete2D human and object token maps via HOI Vector Quantization (VQ),preserving fine-grained spatial-temporal structure beyond conventional1D representations. On this basis, a generative masked modeling frame-work is employed to jointly capture human-object interaction dynamics,leveraging a transformer architecture designed to model complex spatial-temporal and interaction dependencies. To generate more coherent andphysically plausible motions, we further introduce a novel contact-awarereconstruction guidance in discrete space during inference, which fusescontact signals to optimize HOI tokens that forces the generated mo-tion with higher spatio-temporal consistency. With craftily designed mo-tion interaction tokens, dedicated architecture and guidance strategy,HOIMask outperforms state-of-the-art diffusion-based methods, gener-ating more realistic and semantically aligned HOI motions. Please referto our project page for more results.