  3D human pose and shape estimation from monocular images has been an active
research area in computer vision. Existing deep learning methods for this task
rely on high-resolution input, which however, is not always available in many
scenarios such as video surveillance and sports broadcasting. Two common
approaches to deal with low-resolution images are applying super-resolution
techniques to the input, which may result in unpleasant artifacts, or simply
training one model for each resolution, which is impractical in many realistic
applications.
  To address the above issues, this paper proposes a novel algorithm called
RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss,
and a Contrastive learning scheme. The proposed method is able to learn 3D body
pose and shape across different resolutions with one single model. The
self-supervision loss enforces scale-consistency of the output, and the
contrastive learning scheme enforces scale-consistency of the deep features. We
show that both these new losses provide robustness when learning in a
weakly-supervised manner. Moreover, we extend the RSC-Net to handle
low-resolution videos and apply it to reconstruct textured 3D pedestrians from
low-resolution input. Extensive experiments demonstrate that the RSC-Net can
achieve consistently better results than the state-of-the-art methods for
challenging low-resolution images.
