Spatial-Temporal Relation Guided Motion Transfer via Diffusion Model

Transferring existing Human-Object Interaction (HOI) motion to novel objects is essential for robotics, virtual reality. Traditional approaches only model spatial surface correspondences between humans and source objects or between source and target objects, ignoring the internal topological structures of humans, the internal topology of objects, the non-surface spatial topological relationships, and temporal motion relations. In this paper, we propose a spatial-temporal relation guided motion transfer framework. Firstly, we define a spatial-temporal relation interaction graph representation(STRIG) to model the human internal topology, object internal topology and human-object global topology together with the temporal motion relation. We propose a STRIGs-guided motion transfer diffusion model for generating spatially, semantically and temporally consistent HOI motions that are adapted to novel objects. To tackle the absence of ground-truth motions after transfer, we introduce a spatial-temporal relation optimization strategy. Extensive experiments demonstrate that our method consistently outperforms other approaches in terms of motion transfer quality, performance, and sequence stability, with particularly robustness under large variations in target object topology.