Future Motion Dynamic Modeling via Hybrid Supervision for Multi-Person Motion Prediction Uncertainty Reduction

Multi-person motion prediction remains a challenging problem due to the intricate motion dynamics and complex interpersonal interactions, where uncertainty escalates rapidly across the forecasting horizon. Existing approaches always overlook the motion dynamic modeling among the prediction frames to reduce the uncertainty, but leave it entirely up to the deep neural networks, which lacks a dynamic inductive bias, leading to suboptimal performance. This paper addresses this limitation by proposing an effective multi-person motion prediction method named Hybrid Supervision Transformer (HSFormer), which formulates the dynamic modeling within the prediction horizon as a novel hybrid supervision task. To be precise, our method performs a rolling predicting process equipped with a hybrid supervision mechanism, which enforces the model to be able to predict the pose in the next frames based on the (typically error-contained) earlier predictions. Addition to the standard supervision loss, two self and auxiliary supervision mechanisms, which minimize the distance of the predictions with error-contained inputs and the predictions with error-free inputs (ground truth) and guide the model to make accurate predictions based on the ground truth, are introduced to improve the robustness of our model to the input deviation in inference and stabilize the training process, respectively. The optimization techniques, such as stop-gradient, are extended to our model to improve the training efficiency. Furthermore, we develop a fine-grained spatio-temporal correlation capture module to assist the feature learning and reduce the uncertainties arising from the intricate and varying interactions among the individuals. Our approach achieves state-of-the-art results on multiple multi-person datasets in both short- and long-term prediction.