  Dense human pose estimation is the problem of learning dense correspondences
between RGB images and the surfaces of human bodies, which finds various
applications, such as human body reconstruction, human pose transfer, and human
action recognition. Prior dense pose estimation methods are all based on Mask
R-CNN framework and operate in a top-down manner of first attempting to
identify a bounding box for each person and matching dense correspondences in
each bounding box. Consequently, these methods lack robustness due to their
critical dependence on the Mask R-CNN detection, and the runtime increases
drastically as the number of persons in the image increases. We therefore
propose a novel alternative method for solving the dense pose estimation
problem, called Direct Dense Pose (DDP). DDP first predicts the instance mask
and global IUV representation separately and then combines them together. We
also propose a simple yet effective 2D temporal-smoothing scheme to alleviate
the temporal jitters when dealing with video data. Experiments demonstrate that
DDP overcomes the limitations of previous top-down baseline methods and
achieves competitive accuracy. In addition, DDP is computationally more
efficient than previous dense pose estimation methods, and it reduces jitters
when applied to a video sequence, which is a problem plaguing the previous
methods.
