AffordPose: A Large-scale Dataset of Hand-Object Interactions with
  Affordance-driven Hand Pose
Juntao Jian Xiuping Liu Manyi Li Ruizhen Hu Jian Liu
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.