  Learning to capture human motion is essential to 3D human pose and shape
estimation from monocular video. However, the existing methods mainly rely on
recurrent or convolutional operation to model such temporal information, which
limits the ability to capture non-local context relations of human motion. To
address this problem, we propose a motion pose and shape network (MPS-Net) to
effectively capture humans in motion to estimate accurate and temporally
coherent 3D human pose and shape from a video. Specifically, we first propose a
motion continuity attention (MoCA) module that leverages visual cues observed
from human motion to adaptively recalibrate the range that needs attention in
the sequence to better capture the motion continuity dependencies. Then, we
develop a hierarchical attentive feature integration (HAFI) module to
effectively combine adjacent past and future feature representations to
strengthen temporal correlation and refine the feature representation of the
current frame. By coupling the MoCA and HAFI modules, the proposed MPS-Net
excels in estimating 3D human pose and shape in the video. Though conceptually
simple, our MPS-Net not only outperforms the state-of-the-art methods on the
3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets, but also uses fewer
network parameters. The video demos can be found at
https://mps-net.github.io/MPS-Net/.
