Abstract
Deep recurrent neural networks have achieved impressive success in forecasting human motion with
a sequence to sequence architecture. However,
forecasting in longer time horizons often leads to
implausible human poses or converges to mean
poses, because of error accumulation and difficulties in keeping track of longerterm information. To
address these challenges, we propose to retrospect
human dynamics with attention. A retrospection
module is designed upon RNN to regularly retrospect past frames and correct mistakes in time.
This significantly improves the memory of RNN
and provides sufficient information for the decoder
networks to generate longer term prediction. Moreover, we present a spatial attention module to explore and exploit cooperation among joints in performing a particular motion. Residual connections
are also included to guarantee the performance of
short term prediction. We evaluate the proposed algorithm on the largest and most challenging Human 3.6M dataset in the field. Experimental results
demonstrate the necessity of investigating motion
prediction in a self audit manner and the effectiveness of the proposed algorithm in both short term
and long term predictions