  Recently, vision transformers have shown great success in a set of human
reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose
estimation (3D HPE), and human mesh reconstruction (HMR) tasks. In these tasks,
feature map representations of the human structural information are often
extracted first from the image by a CNN (such as HRNet), and then further
processed by transformer to predict the heatmaps (encodes each joint's location
into a feature map with a Gaussian distribution) for HPE or HMR. However,
existing transformer architectures are not able to process these feature map
inputs directly, forcing an unnatural flattening of the location-sensitive
human structural information. Furthermore, much of the performance benefit in
recent HPE and HMR methods has come at the cost of ever-increasing computation
and memory needs. Therefore, to simultaneously address these problems, we
propose FeatER, a novel transformer design that preserves the inherent
structure of feature map representations when modeling attention while reducing
memory and computational costs. Taking advantage of FeatER, we build an
efficient network for a set of human reconstruction tasks including 2D HPE, 3D
HPE, and HMR. A feature map reconstruction module is applied to improve the
performance of the estimated human pose and mesh. Extensive experiments
demonstrate the effectiveness of FeatER on various human pose and mesh
datasets. For instance, FeatER outperforms the SOTA method MeshGraphormer by
requiring 5% of Params and 16% of MACs on Human3.6M and 3DPW datasets. The
project webpage is https://zczcwh.github.io/feater_page/.
