Learning 3D human-object interaction relation is pivotal to embodied AI and
interaction modeling. Most existing methods approach the goal by learning to
predict isolated interaction elements, e.g., human contact, object affordance,
and human-object spatial relation, primarily from the perspective of either the
human or the object. Which underexploit certain correlations between the
interaction counterparts (human and object), and struggle to address the
uncertainty in interactions. Actually, objects' functionalities potentially
affect humans' interaction intentions, which reveals what the interaction is.
Meanwhile, the interacting humans and objects exhibit matching geometric
structures, which presents how to interact. In light of this, we propose
harnessing these inherent correlations between interaction counterparts to
mitigate the uncertainty and jointly anticipate the above interaction elements
in 3D space. To achieve this, we present LEMON (LEarning 3D huMan-Object
iNteraction relation), a unified model that mines interaction intentions of the
counterparts and employs curvatures to guide the extraction of geometric
correlations, combining them to anticipate the interaction elements. Besides,
the 3D Interaction Relation dataset (3DIR) is collected to serve as the test
bed for training and evaluation. Extensive experiments demonstrate the
superiority of LEMON over methods estimating each element in isolation.

利用 3D 人物 - 物体交互关系的内在相关性，LEMON 是一个统一模型，通过提取几何相关性并利用曲率来预测交互元素，超越了单独估计每个元素的方法。