  Whole-body pose and shape estimation aims to jointly predict different
behaviors (e.g., pose, hand gesture, facial expression) of the entire human
body from a monocular image. Existing methods often exhibit degraded
performance under the complexity of in-the-wild scenarios. We argue that the
accuracy and reliability of these models are significantly affected by the
quality of the predicted \textit{bounding box}, e.g., the scale and alignment
of body parts. The natural discrepancy between the ideal bounding box
annotations and model detection results is particularly detrimental to the
performance of whole-body pose and shape estimation. In this paper, we propose
a novel framework to enhance the robustness of whole-body pose and shape
estimation. Our framework incorporates three new modules to address the above
challenges from three perspectives: \textbf{1) Localization Module} enhances
the model's awareness of the subject's location and semantics within the image
space. \textbf{2) Contrastive Feature Extraction Module} encourages the model
to be invariant to robust augmentations by incorporating contrastive loss with
dedicated positive samples. \textbf{3) Pixel Alignment Module} ensures the
reprojected mesh from the predicted camera and body model parameters are
accurate and pixel-aligned. We perform comprehensive experiments to demonstrate
the effectiveness of our proposed framework on body, hands, face and whole-body
benchmarks. Codebase is available at
\url{https://github.com/robosmplx/robosmplx}.
