Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body
  Reconstruction
Yufei Zhang Hanjing Wang Jeffrey O. Kephart Qiang Ji
While 3D body reconstruction methods have made remarkable progress recently,
it remains difficult to acquire the sufficiently accurate and numerous 3D
supervisions required for training. In this paper, we propose \textbf{KNOWN}, a
framework that effectively utilizes body \textbf{KNOW}ledge and
u\textbf{N}certainty modeling to compensate for insufficient 3D supervisions.
KNOWN exploits a comprehensive set of generic body constraints derived from
well-established body knowledge. These generic constraints precisely and
explicitly characterize the reconstruction plausibility and enable 3D
reconstruction models to be trained without any 3D data. Moreover, existing
methods typically use images from multiple datasets during training, which can
result in data noise (\textit{e.g.}, inconsistent joint annotation) and data
imbalance (\textit{e.g.}, minority images representing unusual poses or
captured from challenging camera views). KNOWN solves these problems through a
novel probabilistic framework that models both aleatoric and epistemic
uncertainty. Aleatoric uncertainty is encoded in a robust Negative
Log-Likelihood (NLL) training loss, while epistemic uncertainty is used to
guide model refinement. Experiments demonstrate that KNOWN's body
reconstruction outperforms prior weakly-supervised approaches, particularly on
the challenging minority images.