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.
翻译:旨在将肖像摄影转换成卡通风格的全体肖像图象结构,最近引起了人们的注意。然而,大多数方法仅侧重于转换面部区域,限制在现实世界应用中使用的可行性。最近提出的一个两阶段方法将铸造区扩大到完整的身体,但产出不那么可信,无法达到非面部区域的质量稳健性。此外,它们不能反映不同的皮肤色调。在这个研究中,我们提出了一个以数据为中心的解决方案,以建立一个生产层面的全体肖像结构系统。根据两阶段方案,我们构建了一个创新和先进的数据集编制模式,可以有效解决上述问题。实验显示,通过我们的管道,高品质的肖像石化可以在没有额外损失或建筑变革的情况下实现。