SG-MoE: Structured Geometric Mixture-of-Experts for Monocular Human-Object Interaction Reconstruction

Monocular human–object interaction reconstruction requires recovering both individual surfaces and their coherent spatial configuration from a single image, which is challenging due to severe depth ambiguity and mutual occlusion. Existing methods typically rely on a fixed combination of interaction and geometric reasoning mechanisms throughout progressive reconstruction, despite the evolving demands of different decoding stages. We introduce SG-MoE, a structured geometric mixture-of-experts framework that adaptively composes complementary geometric reasoning operations during reconstruction. The proposed experts capture global human–object interaction, entity-specific topology, and evolving relative geometry, enabling the decoder to refine human and object representations according to the current reconstruction state. We further introduce a bidirectional geometric interaction mechanism that couples intermediate geometry with cross-entity feature reasoning. Experiments on BEHAVE and InterCap demonstrate consistent improvements in human and object reconstruction as well as contact recovery over state-of-the-art methods, while maintaining favorable model complexity and inference efficiency.