Multi-Objective Diverse Human Motion Prediction with Knowledge Distillation

Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving. Recent research has ex-plored several techniques to enhance diversity and maintain the accuracy of human motion prediction at the same time. However, most of them need to define a combined loss, such as the weighted sum of accuracy loss and diversity loss, and then decide their weights as hyperparameters before training. In this work, we aim to design a prediction frame-work that can balance the accuracy sampling and diversity sampling during the testing phase. In order to achieve this target, we propose a multi-objective conditional variational inference prediction model. We also propose a short-term oracle to encourage the prediction framework to explore more diverse future motions. We evaluate the performance of our proposed approach on two standard human motion datasets. The experiment results show that our approach is effective and on a par with state-of-the-art performance in terms of accuracy and diversity.