HDG-ODE: A Hierarchical Continuous-Time Model for Human Pose Forecasting

Recently, human pose estimation has attracted more and more attention due to its importance in many real applications. Although many efforts have been put on extracting 2D poses from static images, there are still some severe problems to be solved. A critical one is occlusion, which is more obvious in multi-person scenarios and makes it even more difficult to recover the corresponding 3D poses. When we consider a sequence of images, the temporal correlation among the contexts can be utilized to help us ease the problem, but most of the current works only rely on discrete-time models and estimate the joint locations of all people within a whole sparse graph. In this paper, we propose a new framework, Hierarchical Dynamic Graph Ordinary Differential Equation (HDG-ODE), to tackle the 3D pose forecasting task from 2D skeleton representations in videos. Our framework adopts ODE, a continuous-time model, as the base to predict the 3D joint positions at any time. Considering the structural-property of the skeleton data in representing human poses and the possible irregularity caused by occlusion, we propose the use of dynamic graph convolution as the basic operator. To reduce the computational complexity introduced by the sparsity of the pose graph, our model takes a hierarchical structure where the encoding process at the observation timestamp is done in a cascade manner while the propagation between observations is conducted in parallel. The performance studies on several datasets demonstrate that our model is effective and can out-perform other methods with fewer parameters.