  The recovery of 3D human mesh from monocular images has significantly been
developed in recent years. However, existing models usually ignore spatial and
temporal information, which might lead to mesh and image misalignment and
temporal discontinuity. For this reason, we propose a novel Spatio-Temporal
Alignment Fusion (STAF) model. As a video-based model, it leverages coherence
clues from human motion by an attention-based Temporal Coherence Fusion Module
(TCFM). As for spatial mesh-alignment evidence, we extract fine-grained local
information through predicted mesh projection on the feature maps. Based on the
spatial features, we further introduce a multi-stage adjacent Spatial Alignment
Fusion Module (SAFM) to enhance the feature representation of the target frame.
In addition to the above, we propose an Average Pooling Module (APM) to allow
the model to focus on the entire input sequence rather than just the target
frame. This method can remarkably improve the smoothness of recovery results
from video. Extensive experiments on 3DPW, MPII3D, and H36M demonstrate the
superiority of STAF. We achieve a state-of-the-art trade-off between precision
and smoothness. Our code and more video results are on the project page
https://yw0208.github.io/staf/
