InterMamba: Efficient Human-Human Interaction Generation With Adaptive Spatio-Temporal Mamba

Human-human interaction generation has garnered significant attention in motion synthesis due to its vital role in understanding humans as social beings. However, existing methods typically rely on transformer-based architectures, which often face challenges related to scalability and efficiency. To address these challenges, we propose InterMamba, a novel and efficient human-human interaction generation method built on the Mamba framework, designed to capture long-sequence dependencies effectively while enabling real-time feedback. Specifically, we introduce an adaptive spatio-temporal Mamba framework that utilizes two parallel SSM branches with an adaptive mechanism to integrate the spatial and temporal features of motion sequences. To further enhance the model's ability to capture dependencies within individual motion sequences and the interactions between different individual sequences, we develop two key modules: the self adaptive spatio-temporal Mamba module and the cross adaptive spatio-temporal Mamba module, enabling efficient feature learning. Extensive experiments demonstrate that our method achieves the state-of-the-art results on both two interaction datasets with remarkable quality and efficiency. Compared to the baseline method InterGen, our approach not only improves accuracy but also reduces the parameter size to just 66 M (36% of InterGen's), while achieving an average inference speed of 0.57 seconds, which is 46% of InterGen's execution time.