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Full-body portrait stylization, which aims to translate portrait photographyinto a cartoon style, has drawn attention recently. However, most methods havefocused only on converting face regions, restraining the feasibility of use inreal-world applications. A recently proposed two-stage method expands therendering area to full bodies, but the outputs are less plausible and fail toachieve quality robustness of non-face regions. Furthermore, they cannotreflect diverse skin tones. In this study, we propose a data-centric solutionto build a production-level full-body portrait stylization system. Based on thetwo-stage scheme, we construct a novel and advanced dataset preparationparadigm that can effectively resolve the aforementioned problems. Experimentsreveal that with our pipeline, high-quality portrait stylization can beachieved without additional losses or architectural changes.

WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization