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

• URL: http://arxiv.org/abs/2210.10335v1
• Date: Wed, 19 Oct 2022 07:09:03 GMT
• Title: WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization
• Authors: Jihye Back, Seungkwon Kim, Namhyuk Ahn
• Abstract summary: 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. Experiments reveal that with our pipeline, high-quality portrait stylization can be achieved without additional losses or architectural changes.
• Score: 5.2661965280415926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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