  Human-scene Interaction (HSI) generation is a challenging task and crucial
for various downstream tasks. However, one of the major obstacles is its
limited data scale. High-quality data with simultaneously captured human and 3D
environments is hard to acquire, resulting in limited data diversity and
complexity. In this work, we argue that interaction with a scene is essentially
interacting with the space occupancy of the scene from an abstract physical
perspective, leading us to a unified novel view of Human-Occupancy Interaction.
By treating pure motion sequences as records of humans interacting with
invisible scene occupancy, we can aggregate motion-only data into a large-scale
paired human-occupancy interaction database: Motion Occupancy Base (MOB). Thus,
the need for costly paired motion-scene datasets with high-quality scene scans
can be substantially alleviated. With this new unified view of Human-Occupancy
interaction, a single motion controller is proposed to reach the target state
given the surrounding occupancy. Once trained on MOB with complex occupancy
layout, which is stringent to human movements, the controller could handle
cramped scenes and generalize well to general scenes with limited complexity
like regular living rooms. With no GT 3D scenes for training, our method can
generate realistic and stable HSI motions in diverse scenarios, including both
static and dynamic scenes. The project is available at
https://foruck.github.io/occu-page/.
