  Full-body portrait stylization, which aims to translate portrait photography
into a cartoon style, has drawn attention recently. However, most methods have
focused only on converting face regions, restraining the feasibility of use in
real-world applications. A recently proposed two-stage method expands the
rendering area to full bodies, but the outputs are less plausible and fail to
achieve quality robustness of non-face regions. Furthermore, they cannot
reflect diverse skin tones. In this study, we propose a data-centric solution
to build a production-level full-body portrait stylization system. Based on the
two-stage scheme, we construct a novel and advanced dataset preparation
paradigm that can effectively resolve the aforementioned problems. Experiments
reveal that with our pipeline, high-quality portrait stylization can be
achieved without additional losses or architectural changes.
