Motion Prediction Using Temporal Inception Module
This work addresses motion prediction for robotics and autonomous driving, offering an incremental improvement by focusing on temporal scale exploitation.
The paper tackled the problem of human motion prediction by proposing a Temporal Inception Module (TIM) to exploit diverse temporal scales, resulting in consistent outperformance of state-of-the-art methods on benchmark datasets like Human3.6M and CMU.
Human motion prediction is a necessary component for many applications in robotics and autonomous driving. Recent methods propose using sequence-to-sequence deep learning models to tackle this problem. However, they do not focus on exploiting different temporal scales for different length inputs. We argue that the diverse temporal scales are important as they allow us to look at the past frames with different receptive fields, which can lead to better predictions. In this paper, we propose a Temporal Inception Module (TIM) to encode human motion. Making use of TIM, our framework produces input embeddings using convolutional layers, by using different kernel sizes for different input lengths. The experimental results on standard motion prediction benchmark datasets Human3.6M and CMU motion capture dataset show that our approach consistently outperforms the state of the art methods.