  Though much progress has been achieved in single-image 3D human recovery,
estimating 3D model for in-the-wild images remains a formidable challenge. The
reason lies in the fact that obtaining high-quality 3D annotations for
in-the-wild images is an extremely hard task that consumes enormous amount of
resources and manpower. To tackle this problem, previous methods adopt a hybrid
training strategy that exploits multiple heterogeneous types of annotations
including 3D and 2D while leaving the efficacy of each annotation not
thoroughly investigated. In this work, we aim to perform a comprehensive study
on cost and effectiveness trade-off between different annotations.
Specifically, we focus on the challenging task of in-the-wild 3D human recovery
from single images when paired 3D annotations are not fully available. Through
extensive experiments, we obtain several observations: 1) 3D annotations are
efficient, whereas traditional 2D annotations such as 2D keypoints and body
part segmentation are less competent in guiding 3D human recovery. 2) Dense
Correspondence such as DensePose is effective. When there are no paired
in-the-wild 3D annotations available, the model exploiting dense correspondence
can achieve 92% of the performance compared to a model trained with paired 3D
data. We show that incorporating dense correspondence into in-the-wild 3D human
recovery is promising and competitive due to its high efficiency and relatively
low annotating cost. Our model trained with dense correspondence can serve as a
strong reference for future research.
