  We introduce Pixel-aligned Implicit Function (PIFu), a highly effective
implicit representation that locally aligns pixels of 2D images with the global
context of their corresponding 3D object. Using PIFu, we propose an end-to-end
deep learning method for digitizing highly detailed clothed humans that can
infer both 3D surface and texture from a single image, and optionally, multiple
input images. Highly intricate shapes, such as hairstyles, clothing, as well as
their variations and deformations can be digitized in a unified way. Compared
to existing representations used for 3D deep learning, PIFu can produce
high-resolution surfaces including largely unseen regions such as the back of a
person. In particular, it is memory efficient unlike the voxel representation,
can handle arbitrary topology, and the resulting surface is spatially aligned
with the input image. Furthermore, while previous techniques are designed to
process either a single image or multiple views, PIFu extends naturally to
arbitrary number of views. We demonstrate high-resolution and robust
reconstructions on real world images from the DeepFashion dataset, which
contains a variety of challenging clothing types. Our method achieves
state-of-the-art performance on a public benchmark and outperforms the prior
work for clothed human digitization from a single image.
