  Recent advances in image-based 3D human shape estimation have been driven by
the significant improvement in representation power afforded by deep neural
networks. Although current approaches have demonstrated the potential in real
world settings, they still fail to produce reconstructions with the level of
detail often present in the input images. We argue that this limitation stems
primarily form two conflicting requirements; accurate predictions require large
context, but precise predictions require high resolution. Due to memory
limitations in current hardware, previous approaches tend to take low
resolution images as input to cover large spatial context, and produce less
precise (or low resolution) 3D estimates as a result. We address this
limitation by formulating a multi-level architecture that is end-to-end
trainable. A coarse level observes the whole image at lower resolution and
focuses on holistic reasoning. This provides context to an fine level which
estimates highly detailed geometry by observing higher-resolution images. We
demonstrate that our approach significantly outperforms existing
state-of-the-art techniques on single image human shape reconstruction by fully
leveraging 1k-resolution input images.
