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Demystifying complex human-ground interactions is essential for accurate andrealistic 3D human motion reconstruction from RGB videos, as it ensuresconsistency between the humans and the ground plane. Prior methods have modeledhuman-ground interactions either implicitly or in a sparse manner, oftenresulting in unrealistic and incorrect motions when faced with noise anduncertainty. In contrast, our approach explicitly represents these interactionsin a dense and continuous manner. To this end, we propose a novel Ground-awareMotion Model for 3D Human Motion Reconstruction, named GraMMaR, which jointlylearns the distribution of transitions in both pose and interaction betweenevery joint and ground plane at each time step of a motion sequence. It istrained to explicitly promote consistency between the motion and distancechange towards the ground. After training, we establish a joint optimizationstrategy that utilizes GraMMaR as a dual-prior, regularizing the optimizationtowards the space of plausible ground-aware motions. This leads to realisticand coherent motion reconstruction, irrespective of the assumed or learnedground plane. Through extensive evaluation on the AMASS and AIST++ datasets,our model demonstrates good generalization and discriminating abilities inchallenging cases including complex and ambiguous human-ground interactions.The code will be available at https://github.com/xymsh/GraMMaR.

GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction