Collaborative Human-Object Interaction Reconstruction from a Monocular Video

Collaborative human-object interaction (HOI) reconstruction requires recovering how several people manipulate a shared object over time. Existing video-based HOI reconstruction methods fall short in this scenario, as they neither align multiple human-object pairs in a shared 4D space nor reason about how contacts among multiple humans and the shared object evolve over time. To address this, we present COMBO, a 4D reconstruction method for reconstructing collaborative HOIs from a monocular video. Our key insight is that, in collaborative scenarios, the humans and the object are tightly coupled through contacts that evolve over time, and these time-varying contacts provide the constraint needed for joint reconstruction. We design a two-stage approach: human-anchored initialization first establishes a shared 4D space, using human motions to calibrate depth, and initializes the object. Subsequently, temporal contact-guided optimization builds a proximity-based temporal contact graph from 2D and 3D cues, without a learned contact predictor. Unlike static or single-pair contact formulations, our graph captures temporally varying contacts across participants, guiding the joint alignment of all entities in a shared 4D space. Experiments on the CORE4D benchmark show lower reconstruction errors than competing video-based methods. Our results on in-the-wild and generated videos demonstrate the generalization capability of COMBO. Code will be publicly available upon acceptance.