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

arXiv: https://arxiv.org/abs/2210.10335 · HTML: https://mlsanity.com/paper/2210.10335/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation](https://mlsanity.com/paper/1907.10830/) (2019)
- [Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains](https://mlsanity.com/paper/2010.05334/) (2020)
- [JoJoGAN: One Shot Face Stylization](https://mlsanity.com/paper/2112.11641/) (2021)
- [Cross-Domain Style Mixing for Face Cartoonization](https://mlsanity.com/paper/2205.12450/) (2022)

## Built on by (most cited first, 6 of 6)

- [AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning](https://mlsanity.com/paper/2303.14297/) (2023)
- [ToonAging: Face Re-Aging upon Artistic Portrait Style Transfer](https://mlsanity.com/paper/2402.02733/) (2024)
- [Minecraft-ify: Minecraft Style Image Generation with Text-guided Image Editing for In-Game Application](https://mlsanity.com/paper/2402.05448/) (2024)
- [A Framework for Portrait Stylization with Skin-Tone Awareness and Nudity Identification](https://mlsanity.com/paper/2403.14264/) (2024)
- [Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information](https://mlsanity.com/paper/2403.15048/) (2024)
- [One-Shot Stylization for Full-Body Human Images](https://mlsanity.com/paper/2304.06917/) (2023)

## Abstract (arXiv)

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.