  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.
