  Recent advances in 3D human shape reconstruction from single images have
shown impressive results, leveraging on deep networks that model the so-called
implicit function to learn the occupancy status of arbitrarily dense 3D points
in space. However, while current algorithms based on this paradigm, like
PiFuHD, are able to estimate accurate geometry of the human shape and clothes,
they require high-resolution input images and are not able to capture complex
body poses. Most training and evaluation is performed on 1k-resolution images
of humans standing in front of the camera under neutral body poses. In this
paper, we leverage publicly available data to extend existing implicit
function-based models to deal with images of humans that can have arbitrary
poses and self-occluded limbs. We argue that the representation power of the
implicit function is not sufficient to simultaneously model details of the
geometry and of the body pose. We, therefore, propose a coarse-to-fine approach
in which we first learn an implicit function that maps the input image to a 3D
body shape with a low level of detail, but which correctly fits the underlying
human pose, despite its complexity. We then learn a displacement map,
conditioned on the smoothed surface and on the input image, which encodes the
high-frequency details of the clothes and body. In the experimental section, we
show that this coarse-to-fine strategy represents a very good trade-off between
shape detail and pose correctness, comparing favorably to the most recent
state-of-the-art approaches. Our code will be made publicly available.
