  Human Motion Prediction is a crucial task in computer vision and robotics. It
has versatile application potentials such as in the area of human-robot
interactions, human action tracking for airport security systems, autonomous
car navigation, computer gaming to name a few. However, predicting human motion
based on past actions is an extremely challenging task due to the difficulties
in detecting spatial and temporal features correctly. To detect temporal
features in human poses, we propose an Inception Residual Block(IRB), due to
its inherent capability of processing multiple kernels to capture salient
features. Here, we propose to use multiple 1-D Convolution Neural Network (CNN)
with different kernel sizes and input sequence lengths and concatenate them to
get proper embedding. As kernels strides over different receptive fields, they
detect smaller and bigger salient features at multiple temporal scales. Our
main contribution is to propose a residual connection between input and the
output of the inception block to have a continuity between the previously
observed pose and the next predicted pose. With this proposed architecture, it
learns prior knowledge much better about human poses and we achieve much higher
prediction accuracy as detailed in the paper. Subsequently, we further propose
to feed the output of the inception residual block as an input to the Graph
Convolution Neural Network (GCN) due to its better spatial feature learning
capability. We perform a parametric analysis for better designing of our model
and subsequently, we evaluate our approach on the Human 3.6M dataset and
compare our short-term as well as long-term predictions with the state of the
art papers, where our model outperforms most of the pose results, the detailed
reasons of which have been elaborated in the paper.
