title: AniFaceDrawing: Anime Portrait Exploration during Your Sketching
summary: 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.
paper_authors._id: 648922da50aa32474db4c063
paper_authors.name: Zhengyu Huang
paper_authors._id: 648922da50aa32474db4c064
paper_authors.name: Haoran Xie
paper_authors._id: 648922da50aa32474db4c065
paper_authors.name: Tsukasa Fukusato
paper_authors._id: 648922da50aa32474db4c066
paper_authors.name: Kazunori Miyata
paper_title: AniFaceDrawing: Anime Portrait Exploration during Your Sketching
paper_summary: 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.
paper_ai_keywords: StyleGAN latent space stroke-level disentanglement image encoder sketch encoder feature alignment progressive S2I system