Facial Landmark Detection for Manga Images

Stricker, Marco; Augereau, Olivier; Kise, Koichi; Iwata, Motoi

doi:10.48550/arxiv.1811.03214

Cited by 3 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, when C 2 GAN is trained on the face images of anime characters, it generates images with noise, even in regions that do not change from the source object image [19]. Marco et al defined the format of facial landmarks suitable for manga characters and proposed a method to estimate facial landmarks as a set of 60 points from a face image of a manga character [13]. Figure 2 we checked the performance of FOMM when source image and target image are anime face image and human face image, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…The character face landmark is detected from the face images using the method of Marco et al [13]. Although the method of Marco et al is applied to monochrome comic images, the method can also detect character face landmarks for color anime images.…”

Section: Facial Landmark Estimationmentioning

confidence: 99%

“…Additionally, facial landmarks can be automatically estimated from a face image by existing methods [10], [12]. In particular, Marco et al defined the format of facial landmarks suitable for manga characters and proposed a method to estimate facial landmarks based on it [13]. Face image generation methods that use facial landmarks as the target pose have been proposed for human faces [3], [5], [6], [8], [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Face Image Generation of Anime Characters Using an Advanced First Order Motion Model with Facial Landmarks

OSHIBA

Iwata²,

Kise³

2023

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Recently, deep learning for image generation with a guide for the generation has been progressing. Many methods have been proposed to generate the animation of facial expression change from a single face image by transferring some facial expression information to the face image. In particular, the method of using facial landmarks as facial expression information can generate a variety of facial expressions. However, most methods do not focus on anime characters but humans. Moreover, we attempted to apply several existing methods to anime characters by training the methods on an anime character face dataset; however, they generated images with noise, even in regions where there was no change. The first order motion model (FOMM) is an image generation method that takes two images as input and transfers one facial expression or pose to the other. By explicitly calculating the difference between the two images based on optical flow, FOMM can generate images with low noise in the unchanged regions. In the following, we focus on the aspect of the face image generation in FOMM. When we think about the employment of facial landmarks as targets, the performance of FOMM is not enough because FOMM cannot use a facial landmark as a facial expression target because the appearances of a face image and a facial landmark are quite different. Therefore, we propose an advanced FOMM method to use facial landmarks as a facial expression target. In the proposed method, we change the input data and data flow to use facial landmarks. Additionally, to generate face images with expressions that follow the target landmarks more closely, we introduce the landmark estimation loss, which is computed by comparing the landmark detected from the generated image with the target landmark. Our experiments on an anime character face image dataset demonstrated that our method is effective for landmark-guided face image generation for anime characters. Furthermore, our method outperformed other methods quantitatively and generated face images with less noise.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Facial Landmark Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Face Image Generation of Anime Characters Using an Advanced First Order Motion Model with Facial Landmarks

OSHIBA

Iwata²,

Kise³

2023

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…The latter we adopt for our work. The facial landmark detection method for mangas [21] uses the deep alignment network [13] which uses multiple steps to refine the heatmaps of the previous steps.…”

Section: Related Workmentioning

confidence: 99%

ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints

Sindel¹,

Maier²,

Christlein³

2022

Preprint

View full text Add to dashboard Cite

Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-pointbased image registration. We propose a deep-learning-based method for facial landmark detection in high-resolution images of paintings and prints. It divides the task into a global network for coarse landmark prediction and multiple region networks for precise landmark refinement in regions of the eyes, nose, and mouth that are automatically determined based on the predicted global landmark coordinates. We created a synthetically augmented facial landmark art dataset including artistic style transfer and geometric landmark shifts. Our method demonstrates an accurate detection of the inner facial landmarks for our high-resolution dataset of artworks while being comparable for a public low-resolution artwork dataset in comparison to competing methods.

show abstract

ArtFacePoints: High-Resolution Facial Landmark Detection in Paintings and Prints

Sindel

Maier

Christlein

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Facial Landmark Detection for Manga Images

Cited by 3 publications

References 22 publications

Face Image Generation of Anime Characters Using an Advanced First Order Motion Model with Facial Landmarks

Face Image Generation of Anime Characters Using an Advanced First Order Motion Model with Facial Landmarks

ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints

ArtFacePoints: High-Resolution Facial Landmark Detection in Paintings and Prints

Contact Info

Product

Resources

About