  General image completion and extrapolation methods often fail on portrait
images where parts of the human body need to be recovered - a task that
requires accurate human body structure and appearance synthesis. We present a
two-stage deep learning framework for tacking this problem. In the first stage,
given a portrait image with an incomplete human body, we extract a complete,
coherent human body structure through a human parsing network, which focuses on
structure recovery inside the unknown region with the help of pose estimation.
In the second stage, we use an image completion network to fill the unknown
region, guided by the structure map recovered in the first stage. For realistic
synthesis the completion network is trained with both perceptual loss and
conditional adversarial loss. We evaluate our method on public portrait image
datasets, and show that it outperforms other state-of-art general image
completion methods. Our method enables new portrait image editing applications
such as occlusion removal and portrait extrapolation. We further show that the
proposed general learning framework can be applied to other types of images,
e.g. animal images.
