ACM SIGGRAPH 2023
AniFaceDrawing: Anime Portrait Exploration during Your Sketching
Zhengyu Huang*, Haoran Xie*, Tsukasa Fukusato**,
Kazunori Miyata*
(JAIST*, Waseda University**)
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Abstract
In this paper, we focus on how artificial intelligence (AI) can be used to assist users in the creation of anime portraits, that is, converting rough sketches into anime portraits during their sketching process. The input is a sequence of incomplete freehand sketches that are gradually refined stroke by stroke, while the output is a sequence of high-quality anime portraits that correspond to the input sketches as guidance. Although recent GANs can generate high quality images, it is a challenging problem to maintain the high quality of generated images from sketches with a low degree of completion due to ill-posed problems in conditional image generation. Even with the latest sketch-to-image (S2I) technology, it is still difficult to create high-quality images from incomplete rough sketches for anime portraits since anime style tend to be more abstract than in realistic style.
To address this issue, we adopt a latent space exploration of StyleGAN with a two-stage training strategy. We consider the input strokes of a freehand sketch to correspond to edge information-related attributes in the latent structural code of StyleGAN, and term the matching between strokes and these attributes stroke-level disentanglement. In the first stage, we trained an image encoder with the pre-trained StyleGAN model as a teacher encoder. In the second stage, we simulated the drawing process of the generated images without any additional data (labels) and trained the sketch encoder for incomplete progressive sketches to generate high-quality portrait images with feature alignment to the disentangled representations in the teacher encoder. We verified the proposed progressive S2I system with both qualitative and quantitative evaluations and achieved high-quality anime portraits from incomplete progressive sketches. Our user study proved its effectiveness in art creation assistance for the anime style.
Video
Stroke-level Disentanglement
Proposed Framework
User Interface
Results
Citation
@inproceedings{huang2023Anifacedraw,
title = {AniFaceDrawing: Anime Portrait Exploration during Your Sketching},
author = {Huang, Zhengyu and Xie, Haoran and Fukusato, Tsukasa and Miyata, Kazunori},
booktitle = {ACM SIGGRAPH 2023 Conference Proceedings},
year = {2023},
series = {SIGGRAPH '23},
}
Acknowledgements
This research was supported by the JAIST Research Fund, Kayamori Foundation of Informational Science Advancement, JSPS KAKENHI JP20K19845, and JP19K20316.Related Projects
DualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (2022)Sketch2VF: Sketch-Based Flow Design with Conditional Generative Adversarial Network (2019)