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This paper presents a high-quality human motion prediction method thataccurately predicts future human poses given observed ones. Our method is basedon the observation that a good initial guess of the future poses is veryhelpful in improving the forecasting accuracy. This motivates us to propose anovel two-stage prediction framework, including an init-prediction network thatjust computes the good guess and then a formal-prediction network that predictsthe target future poses based on the guess. More importantly, we extend thisidea further and design a multi-stage prediction framework where each stagepredicts initial guess for the next stage, which brings more performance gain.To fulfill the prediction task at each stage, we propose a network comprisingSpatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense GraphConvolutional Networks (T-DGCN). Alternatively executing the two networks helpsextract spatiotemporal features over the global receptive field of the wholepose sequence. All the above design choices cooperating together make ourmethod outperform previous approaches by large margins: 6%-7% on Human3.6M,5%-10% on CMU-MoCap, and 13%-16% on 3DPW.

Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction