  3D human shape and pose estimation from monocular images has been an active
area of research in computer vision, having a substantial impact on the
development of new applications, from activity recognition to creating virtual
avatars. Existing deep learning methods for 3D human shape and pose estimation
rely on relatively high-resolution input images; however, high-resolution
visual content is not always available in several practical scenarios such as
video surveillance and sports broadcasting. Low-resolution images in real
scenarios can vary in a wide range of sizes, and a model trained in one
resolution does not typically degrade gracefully across resolutions. Two common
approaches to solve the problem of low-resolution input are applying
super-resolution techniques to the input images which may result in visual
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 network is able to learn the 3D body shape and pose across
different resolutions with a single model. The self-supervision loss encourages
scale-consistency of the output, and the contrastive learning scheme enforces
scale-consistency of the deep features. We show that both these new training
losses provide robustness when learning 3D shape and pose in a
weakly-supervised manner. Extensive experiments demonstrate that the RSC-Net
can achieve consistently better results than the state-of-the-art methods for
challenging low-resolution images.
