CHAMP: A Large-Scale Dataset for Skeleton-based Composite HumAn Motion Prediction

Skeleton-based human motion prediction task aims to forecast future skeleton frames conditioned by observed skeleton sequence. Different from previous methods that focus on human motion prediction for atomic actions, we observe that people are witnessed to perform composite actions which consist of atomic actions that simultaneously happen. Considering the large number of action types, it is more laborious to collect composite actions than atomic actions. This paper presents a practical composite human motion prediction task, whose training data just contains atomic actions meanwhile the test data contains both atomic actions and composite actions. To evaluate this task, we collect a large-scale Composite HumAn Motion Prediction (CHAMP) dataset, whose training data has 16 types of atomic actions and test data has 50 types of composite actions. Despite the success of previous human motion prediction methods using Graph Convolutional Networks (GCN), these methods achieve inferior performances on our CHAMP dataset due to the huge domain gap between the training and test data. To solve this problem, we present a composite human motion prediction framework containing three modules. First, a Composite Motion Synthesis (CMS) module is designed to generate synthesized composite human actions from atomic actions. Second, a Composite GCN module is presented to predict human motion by modeling different human body parts. Third, a human body partition policy network is used to choose the best partition strategy for both the CMS and Composite GCN modules. Extensive experiments on the CHAMP dataset verify the effectiveness of our framework which obviously outperforms GCN-based methods.