Learning Generatable Mutual Distance for Scene-Aware Human Motion Generation

Generating human motion from language in 3D scenes re-quires both semantic alignment with textual intent and physically plau-sible human-scene interactions. Existing methods struggle because im-plicit language-scene fusion entangles motion synthesis with scene rea-soning, while explicit grounding via fixed-vocabulary detectors or affor-dance maps lacks generalization and fails to capture full-body, temporallycoherent interactions. We address this representation gap by identifyingmutual distance as an effective interaction representation for modelingfull-body spatiotemporal human-scene relations, and propose MDNet,a two-stage diffusion framework that explicitly generates and groundssuch interactions. Since mutual distance encodes only relative geometryand is ambiguous for stochastic generation, we introduce VLM-based se-mantic anchors to provide absolute spatial grounding. We further modelmutual distance in the frequency domain to enhance temporal stabilitybefore synthesizing final motions. Experiments on HUMANISE demon-strate state-of-the-art semantic alignment, physical plausibility, and tem-poral coherence, with cross-dataset generalization to unseen scenes.