MOCHI: Motion Enhancement of Collaborative Human-object Interactions

Collaborative human-object interaction shows dynamic and complex movements that require mutual anticipation and continuous adjustment between participants and the shared object. Modeling such collaborative multi-human object interaction (MHOI) scenarios requires high-quality data acquisition as a foundational step; however, this is challenging due to the inherent complexity of MHOI where human-human and human-object interactions occur simultaneously. Such complexity leads to noisy MHOI captures characterized by several artifacts: contact misalignment between hands and objects, motion jitter and temporal inconsistencies in the captured sequences, and missing or incomplete finger-level articulation details. To address these challenges, the authors present MOCHI (MOtion Enhancement of Collaborative Human-object Interactions), a two-stage framework for enhancing noisy MHOI data. The approach first generates physically plausible hand grasps through optimization from noisy body input, producing grasps that are both physically plausible and semantically consistent with the body pose, where these optimized grasps are extended into complete hand-object interaction sequences. Consequently, the full-body motion for all participants is refined through a diffusion-based noise optimization framework that uses single-person motion priors. During the optimization process, optimization objectives encode human-object and human-human interaction information within these single-person priors. Experimental results demonstrate the effectiveness of the pipeline across diverse MHOI data, either acquired by existing capture methods or synthesized by generative models. The system shows robustness across varying numbers of participants and types of interactions, and demonstrates applications including keyframe-based MHOI creation and data augmentation through varying object geometries.