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3D human pose and shape estimation from monocular images has been an activeresearch area in computer vision. Existing deep learning methods for this taskrely on high-resolution input, which however, is not always available in manyscenarios such as video surveillance and sports broadcasting. Two commonapproaches to deal with low-resolution images are applying super-resolutiontechniques to the input, which may result in unpleasant artifacts, or simplytraining one model for each resolution, which is impractical in many realisticapplications.  To address the above issues, this paper proposes a novel algorithm calledRSC-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 bodypose and shape across different resolutions with one single model. Theself-supervision loss enforces scale-consistency of the output, and thecontrastive learning scheme enforces scale-consistency of the deep features. Weshow that both these new losses provide robustness when learning in aweakly-supervised manner. Moreover, we extend the RSC-Net to handlelow-resolution videos and apply it to reconstruct textured 3D pedestrians fromlow-resolution input. Extensive experiments demonstrate that the RSC-Net canachieve consistently better results than the state-of-the-art methods forchallenging low-resolution images.

3D Human Pose, Shape and Texture from Low-Resolution Images and Videos