TFAN: Twin-Flow Axis Normalization for Human Motion Prediction

Human motion prediction involves forecasting upcoming body poses from historically observed sequences. Presently, various methods focus on modeling the positions of skeletal joints to generate future movements. Beyond solely using the joint position information, this letter further explores the informative bone vector information between joints for human motion prediction. Therefore, we propose TFAN, which integrates joint position and bone vector information to achieve more precise predictions. Additionally, joint positions and bone vectors, both represented as 3D vectors, were frequently imprecisely modeled due to neglect of variations in data distribution across axes in prior methods. Instead, we introduce Axis Normalization, which standardizes each coordinate axis individually, enhancing the model's sensitivity to data distribution disparities. Through experimental evaluations on the Human3.6M, AMASS, and 3DPW datasets, we consistently demonstrate that TFAN outperforms other existing methods.