论文标题
WebToonMe：一种以数据为中心的全身肖像风格化方法
WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization
论文作者
论文摘要
全身肖像风格化旨在将肖像摄影转化为卡通风格，最近引起了人们的关注。但是，大多数方法仅着眼于转换面部区域，从而限制了在现实世界应用中使用的可行性。最近提出的两阶段方法将渲染区域扩展到完整的身体，但是输出不太合理，无法实现非面积区域的质量鲁棒性。此外，它们无法反映多样的肤色。在这项研究中，我们提出了一种以数据为中心的解决方案，以建立生产级全身肖像风格化系统。基于两阶段方案，我们构建了一个新颖的高级数据集准备范式，该范式可以有效地解决上述问题。实验表明，通过我们的管道，可以在没有其他损失或建筑变化的情况下实现高质量的肖像画。
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.
