  In this paper, we present a spatio-temporal tendency reasoning (STR) network
for recovering human body pose and shape from videos. Previous approaches have
focused on how to extend 3D human datasets and temporal-based learning to
promote accuracy and temporal smoothing. Different from them, our STR aims to
learn accurate and natural motion sequences in an unconstrained environment
through temporal and spatial tendency and to fully excavate the spatio-temporal
features of existing video data. To this end, our STR learns the representation
of features in the temporal and spatial dimensions respectively, to concentrate
on a more robust representation of spatio-temporal features. More specifically,
for efficient temporal modeling, we first propose a temporal tendency reasoning
(TTR) module. TTR constructs a time-dimensional hierarchical residual
connection representation within a video sequence to effectively reason
temporal sequences' tendencies and retain effective dissemination of human
information. Meanwhile, for enhancing the spatial representation, we design a
spatial tendency enhancing (STE) module to further learns to excite spatially
time-frequency domain sensitive features in human motion information
representations. Finally, we introduce integration strategies to integrate and
refine the spatio-temporal feature representations. Extensive experimental
findings on large-scale publically available datasets reveal that our STR
remains competitive with the state-of-the-art on three datasets. Our code are
available at https://github.com/Changboyang/STR.git.
