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.

本研究提出了AffordPose，一个基于部件级作用标签的大规模手-物交互数据集，揭示了手中心作用对手势姿势的详细排列影响，同时呈现一定程度的多样性。通过参数统计和接触计算，综合数据分析显示了各项作用对手-物交互的共同特征和多样性。我们还进行了手-物作用理解和作用导向手-物交互生成任务的实验，验证了我们的数据集在学习精细手-物交互方面的有效性。

AffordPose：一个大规模手-物体互动的数据集，其中包括基于功能的手势姿势