  How human interact with objects depends on the functional roles of the target
objects, which introduces the problem of affordance-aware hand-object
interaction. It requires a large number of human demonstrations for the
learning and understanding of plausible and appropriate hand-object
interactions. In this work, we present AffordPose, a large-scale dataset of
hand-object interactions with affordance-driven hand pose. We first annotate
the specific part-level affordance labels for each object, e.g. twist, pull,
handle-grasp, etc, instead of the general intents such as use or handover, to
indicate the purpose and guide the localization of the hand-object
interactions. The fine-grained hand-object interactions reveal the influence of
hand-centered affordances on the detailed arrangement of the hand poses, yet
also exhibit a certain degree of diversity. We collect a total of 26.7K
hand-object interactions, each including the 3D object shape, the part-level
affordance label, and the manually adjusted hand poses. The comprehensive data
analysis shows the common characteristics and diversity of hand-object
interactions per affordance via the parameter statistics and contacting
computation. We also conduct experiments on the tasks of hand-object affordance
understanding and affordance-oriented hand-object interaction generation, to
validate the effectiveness of our dataset in learning the fine-grained
hand-object interactions. Project page:
https://github.com/GentlesJan/AffordPose.
