A Framework for Portrait Stylization with Skin-Tone Awareness and Nudity
  Identification
Seungkwon Kim Sangyeon Kim Seung-Hun Nam
Portrait stylization is a challenging task involving the transformation of an
input portrait image into a specific style while preserving its inherent
characteristics. The recent introduction of Stable Diffusion (SD) has
significantly improved the quality of outcomes in this field. However, a
practical stylization framework that can effectively filter harmful input
content and preserve the distinct characteristics of an input, such as
skin-tone, while maintaining the quality of stylization remains lacking. These
challenges have hindered the wide deployment of such a framework. To address
these issues, this study proposes a portrait stylization framework that
incorporates a nudity content identification module (NCIM) and a
skin-tone-aware portrait stylization module (STAPSM). In experiments, NCIM
showed good performance in enhancing explicit content filtering, and STAPSM
accurately represented a diverse range of skin tones. Our proposed framework
has been successfully deployed in practice, and it has effectively satisfied
critical requirements of real-world applications.