  With the increasing prevalence and more powerful camera systems of mobile
devices, people can conveniently take photos in their daily life, which
naturally brings the demand for more intelligent photo post-processing
techniques, especially on those portrait photos. In this paper, we present a
portrait recapture method enabling users to easily edit their portrait to
desired posture/view, body figure and clothing style, which are very
challenging to achieve since it requires to simultaneously perform non-rigid
deformation of human body, invisible body-parts reasoning and semantic-aware
editing. We decompose the editing procedure into semantic-aware geometric and
appearance transformation. In geometric transformation, a semantic layout map
is generated that meets user demands to represent part-level spatial
constraints and further guides the semantic-aware appearance transformation. In
appearance transformation, we design two novel modules, Semantic-aware
Attentive Transfer (SAT) and Layout Graph Reasoning (LGR), to conduct
intra-part transfer and inter-part reasoning, respectively. SAT module produces
each human part by paying attention to the semantically consistent regions in
the source portrait. It effectively addresses the non-rigid deformation issue
and well preserves the intrinsic structure/appearance with rich texture
details. LGR module utilizes body skeleton knowledge to construct a layout
graph that connects all relevant part features, where graph reasoning mechanism
is used to propagate information among part nodes to mine their relations. In
this way, LGR module infers invisible body parts and guarantees global
coherence among all the parts. Extensive experiments on DeepFashion,
Market-1501 and in-the-wild photos demonstrate the effectiveness and
superiority of our approach. Video demo is at:
\url{https://youtu.be/vTyq9HL6jgw}.
