The end-to-end Human Mesh Recovery (HMR) approach has been successfully used
for 3D body reconstruction. However, most HMR-based frameworks reconstruct
human body by directly learning mesh parameters from images or videos, while
lacking explicit guidance of 3D human pose in visual data. As a result, the
generated mesh often exhibits incorrect pose for complex activities. To tackle
this problem, we propose to exploit 3D pose to calibrate human mesh.
Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial
PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in
a serial or parallel manner, these two frameworks can effectively correct human
mesh with guidance of a concise pose calibration module. Furthermore, since the
calibration module is designed via non-rigid pose transformation, our PC-HMR
frameworks can flexibly tackle bone length variations to alleviate misplacement
in the calibrated mesh. Finally, our frameworks are based on generic and
complementary integration of data-driven learning and geometrical modeling. Via
plug-and-play modules, they can be efficiently adapted for both
image/video-based human mesh recovery. Additionally, they have no requirement
of extra 3D pose annotations in the testing phase, which releases inference
difficulties in practice. We perform extensive experiments on the popular
bench-marks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks
achieve the SOTA results.

本文提出了两种新颖的姿态校准框架，采用先进的 3D 姿态估计器和 HMR 序列或并行方式有效地校准人体网格，通过非刚性姿态变换，可以灵活地处理骨长变化以缓解校准后的网格中的误放置，并通过数据驱动学习和几何建模的通用和互补集成来实现，可用于各种图像 / 视频人体网格的恢复，其在多项基准测试中取得了最新的表现。