  Top-down methods dominate the field of 3D human pose and shape estimation,
because they are decoupled from human detection and allow researchers to focus
on the core problem. However, cropping, their first step, discards the location
information from the very beginning, which makes themselves unable to
accurately predict the global rotation in the original camera coordinate
system. To address this problem, we propose to Carry Location Information in
Full Frames (CLIFF) into this task. Specifically, we feed more holistic
features to CLIFF by concatenating the cropped-image feature with its bounding
box information. We calculate the 2D reprojection loss with a broader view of
the full frame, taking a projection process similar to that of the person
projected in the image. Fed and supervised by global-location-aware
information, CLIFF directly predicts the global rotation along with more
accurate articulated poses. Besides, we propose a pseudo-ground-truth annotator
based on CLIFF, which provides high-quality 3D annotations for in-the-wild 2D
datasets and offers crucial full supervision for regression-based methods.
Extensive experiments on popular benchmarks show that CLIFF outperforms prior
arts by a significant margin, and reaches the first place on the AGORA
leaderboard (the SMPL-Algorithms track). The code and data are available at
https://github.com/huawei-noah/noah-research/tree/master/CLIFF.
