  Human pose estimation has achieved significant progress in recent years.
However, most of the recent methods focus on improving accuracy using
complicated models and ignoring real-time efficiency. To achieve a better
trade-off between accuracy and efficiency, we propose a novel neural
architecture search (NAS) method, termed ViPNAS, to search networks in both
spatial and temporal levels for fast online video pose estimation. In the
spatial level, we carefully design the search space with five different
dimensions including network depth, width, kernel size, group number, and
attentions. In the temporal level, we search from a series of temporal feature
fusions to optimize the total accuracy and speed across multiple video frames.
To the best of our knowledge, we are the first to search for the temporal
feature fusion and automatic computation allocation in videos. Extensive
experiments demonstrate the effectiveness of our approach on the challenging
COCO2017 and PoseTrack2018 datasets. Our discovered model family, S-ViPNAS and
T-ViPNAS, achieve significantly higher inference speed (CPU real-time) without
sacrificing the accuracy compared to the previous state-of-the-art methods.
