StylizedFacePoint: Facial Landmark Detection for Stylized Characters

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-04 10:21:30

Abstract: Facial landmark detection forms the foundation for numerous face-related tasks. Recently, this field has gained substantial attention and made significant advancements. Nonetheless, detecting facial landmarks for stylized characters still remains a challenge. Existing approaches, which are mostly trained on real-human face datasets, struggle to perform well due to the structural variations between real and stylized characters. Additionally, a comprehensive dataset for analyzing stylized characters' facial features is lacking. This study proposes a novel dataset, the Facial Landmark Dataset for Stylized Characters (FLSC), which contains 2674 images and 4086 faces. These data is selected from 16 cartoon video clips, together with 98 landmarks per image, labeled by professionals. Besides, we propose StylizedFacePoint: a deep-learning-based method for stylized facial landmark detection that outperforms the existing approaches. This method has also proven to work well for characters with styles outside the training domain. Moreover, we outline two primary types of applications for our dataset and method. For each, we provide a detailed illustrative example.

Main paper: 
 introduction: 
 Facial landmark detection refers to the process of detecting a series of predetermined landmarks on the face. The positions of these landmarks provide crucial information about features such as the approximate facial structure and expression. Consequently, facial landmark detection serves as a fundamental task in numerous facerelated applications [4, 7, 19] .Currently, there are numerous well-performing methods in the field of facial landmark detection [14, 17, 31] . However, the detection of landmarks specifically for stylized characters has received little exploration and continues to face several challenges. To begin with, significant variances in facial structure, the positioning of facial features, and proportions are evident between stylized characters and human faces. Additionally, each stylized character possesses its own distinct artistic style, adding further intricacy. Of utmost importance is the current absence of a facial landmark dataset tailored specifically to stylized characters.To address these problems and fill the absence in this area, we amassed a large collection of images from cartoon movies and created a Facial Landmark dataset for Stylized Characters (FLSC). This dataset comprises 2674 images and 4086 annotated stylized faces. To maintain consistency with previous research, we annotated 98 landmarks for each face. This dataset can be a valuable resource for further face-related research.To better extract the facial landmarks for stylized characters, we propose StylizedFacePoint: a novel method designed to improve the precision of stylized facial landmark detection. This method employs a stacked hourglass network [40] as its primary architecture. After each level of the hourglass network, we incorporate regression for offset heatmaps and neighbor heatmaps on 𝑥-axis and 𝑦-axis, respectively [17] . The heatmaps generated by the current stage of hourglass network are concatenated with the input features after merging, serving as the input for the subsequent stage of the network. This method helps to capture the complex structure of stylized faces and achieve higher accuracy compared to existing methods.Finally, we demonstrate several applications of stylized characters' facial landmark detection. The first category is to utilize landmarks as a bridge variable, and we give a detailed example of how to connect audio and controller values using our proposed dataset and method. The second category is to transfer the domain of some current tasks which rely on facial landmarks, and we also exemplify this category of applications by introducing the case of 3D face reconstruction.In summary, our main contributions can be summarized as follows:• We create a facial landmark dataset for stylized characters, which could be widely utilized in future research. • We propose an approach named StylizedFacePoint for landmark detection. It achieves higher accuracy compared to other methods. • We propose several applications based on landmark detection for stylized characters. 
related work 2.1 human facial landmark datasets: 
 Here we present a brief introduction to existing human facial landmark datasets:• 300 Faces in-the-Wild (300W): consists of 3148 train samples and 689 test samples. Test samples have 300 indoor and 300 outdoor, in-the-wild images. It covers a large variation of identity, expression, illumination conditions, pose, occlusion, and face size.Low-light • Annotated Facial Landmarks in-the-Wild (AFLW) have 24386 annotated images. The dataset contains a wide variety of natural face positions in addition to frontal and nearfrontal faces. • Caltech Occluded Faces in-the-Wild (COFW) are more difficult in terms of occlusion and position as it aims to show faces in realistic settings. The average occlusion rate for faces in this dataset is 28%, with variable degrees of occlusion, and types of occlusion vary a lot. • Wider Facial Landmarks in-the-Wild (WFLW) contains 10000 face photos that were captured in many circumstances.Besides landmarks, it also has comprehensive attribute annotations. i.e., occlusion, pose, make-up, illumination, blur, and expression for analysis.There are also some stylized landmark datasets currently available. However, these datasets suffer from a limitation in terms of the quantity of images. In contrast, our dataset boasts a larger image corpus and encompasses a wider variety of styles. We will provide a brief introduction to them in Section 2.3. A comparison between these datasets and our dataset (FLSC) is presented in Table 1 .In early computer vision tasks, traditional methods like NLoG and DoG [22] were used for landmark detection. Belhumeur et al. [1] proposed non-parametric global detectors combined with local detectors for joint landmark prediction. [23, 26, 31, 32] . Another approach is incorporating an auxiliary network like PFLD [14] . Novel loss functions include ACR loss [11] , using a logarithmic function, and Wing loss [13] , adapting shape and gradient adjustments based on error magnitude. Other techniques like AnchorFace [39] employ anchors and a separation and aggregation solution strategy to tackle pose variations, and RetinaFace [6] detects minute faces in dense crowds while maintaining landmark regression precision.The ATF [21] model is trained using various datasets for landmark detection. It integrates a feature extraction network with multiple landmark regression networks, thereby enhancing the precision of the feature extraction module.Heatmap Based Regression: The HBR method achieves higher landmark accuracy but with a longer running time and a larger parameter size. Optimizations include generative and adversarial networks [2, 8, 27, 45] and semi-supervised learning techniques for pseudo-label generation [9, 10, 17] . Efficient networks such as the stacked hourglass network [40] , U-Net [5] , and HRNet [35] are commonly utilized for generating high-quality heatmaps.Paper [12] uses knowledge distillation to compress and transfer the original large-parameter network model. Several studies have employed cascaded CNNs in the CBR method, thereby further enhancing the efficacy of the HBR method [34] . Ada loss [33] and Adaptive Wing loss [37] adjust the loss function for heatmap characteristics. Moreover, STAR loss [44] assesses the anisotropism of the predicted distribution to capture the semantic ambiguity inherent in heatmap regression. The accurate localization of facial landmarks plays a pivotal role in extracting crucial information regarding facial features, expressions, and various other facial attributes. Consequently, a substantial number of studies in the domain of facial analysis are centered around the detection and localization of facial landmarks. As an illustration, the Deformable Style Transfer [19] method necessitates the precise alignment of specific facial landmarks across facial images and stylized facial images. By accurately detecting facial landmarks in both images, enhanced key point matching outcomes can be achieved, thereby yielding superior style transfer effects. The incorporation of facial landmark information is also essential in numerous 3D facial reconstruction studies. As an instance, Deng et al. [7] employed 2D facial images to regress the positions of 3D landmarks, employing the distance from ground truth as the supervised loss for network training. Similarly, Cai et al. [4] employed 68 facial landmarks extracted from 2D caricatures to regress the shape and orientation of the 3D mesh. We also demonstrate several applications of stylized characters' facial landmark detection using our StylizedFacePoint landmark detector in Section 6. 
dataset design: 
 To the best of our current knowledge, a dataset focused on 3D stylized characters' facial landmark detection is notably absent. Therefore, we introduce the Facial Landmark Dataset for Stylized Characters (FLSC) dataset to address this void.Our approach begins with the careful selection of 16 video clips from famous cartoon movies, containing more than 50 characters. These clips mostly consist of characters engaged in singing activities, which contain rich facial movements. For each video, we systematically capture one image every 5 frames. After that, we ensure a diverse representation of facial expressions. From this collection of images, we manually select those with clear facial features and exclude images with high similarity. These steps ensure a proper and diverse representation of facial expressions. Cases of multi-faces and non-front-face are also included in our dataset. Detailed information and comparisons with other landmark datasets are elaborated in Table 1 .The selection of a suitable landmarks model is crucial for accurate facial landmark detection and following works. From various alternatives, we choose the landmark model proposed in the WFLW dataset, notable for its comprehensive 98-point representation per face. This choice provides a relatively detailed depiction of facial attributes, enhancing the dataset's utility.Expert data annotators were engaged to label the images in our dataset. Subsequently, We manually validate the accuracy and consistency of the annotations. Apart from landmark points, our dataset also includes bounding boxes and facial expressions. These expressions are categorized into seven distinct emotions: Neutral, Sad, Surprise, Happy, Angry, Disgust, and Afraid.By meticulously designing the FLSC dataset and employing robust annotation processes, we aim to contribute a valuable resource for stylized characters' facial landmark detection and analysis. 
method: 
 In this section, we will introduce a new method: StylizedFacePoint, for landmark detection. The Hourglasses Network [42] , serving as a foundational model, is presently prevalent in facial landmark regression tasks. Our approach leverages a stacked Hourglass Network [40] as its backbone. Additionally, we integrate offset regression prediction and neighbor regression prediction into the heatmap regression of landmarks. The pipeline of StylizedFacePoint is illustrated in Figure 2 .A facial image (3 × 𝑊 × 𝐻 ) undergoes processing within the feature extraction module, comprising CNNs and residual networks. The extracted features (𝐶 × 𝑊 ′ × 𝐻 ′ ) are then forwarded to the first-stage hourglass network along with its corresponding residual block. The feature output from the (𝑖 -1)-th stage hourglass network is connected with the input feature of the (𝑖 -1)-th stage, serving jointly as the input for the 𝑖-th stage. This iterative stacking of networks enables progressive extraction of facial features, enhancing the model's capability. Subsequently, after traversing through 𝑆 stages of networks, precise landmark location information is determined. Each hourglass network generates 𝑁 heatmaps, where 𝑁 denotes the number of pre-defined facial landmarks. These normalized heatmap depict the probability distribution of predicted facial landmarks. Finally, the Soft-Argmax function [25] is employed to infer the landmark coordinates 𝑝 from a single heatmap ℎ:𝑝 = 𝑆𝑜 𝑓 𝑡𝐴𝑟𝑔𝑚𝑎𝑥 (ℎ) = ∑︁ 𝑖 ℎ 𝑖 𝑝 𝑖 (1)Where 𝑝 𝑖 is the probability of the predicted landmark located in𝑦 𝑖 ∈ R 2 .By employing the stacked hourglass network, we obtain 𝑁 heatmaps of facial landmarks. The high-resolution image (𝑊 × 𝐻 ) undergoes processing within the feature extraction network and the stacked hourglass network, yielding low-resolution heatmaps (𝑊 ′ × 𝐻 ′ ). To enhance the precision of heatmap regression, we introduce two offset regression modules separately on 𝑥-axis and 𝑦-axis. Additionally, considering the local positional correlation among landmarks, we integrate neighbor regression modules on 𝑥-axis and 𝑦-axis as supervision for training.After each stage of the hourglass network outputs features, they traverse through five residual modules to generate five types of heatmaps, comprising: 1. 𝑁 landmark position heatmaps; 2. 𝑁 prediction offset heatmaps on 𝑥-axis; 3. 𝑁 prediction offset heatmaps on 𝑦-axis; 4. 𝐵 * 𝑁 neighbor prediction offset heatmaps on 𝑥-axis, where 𝐵 represents the number of neighbors of the current landmark; 5. 𝐵 * 𝑁 neighbor prediction offset heatmaps on 𝑦-axis.The heatmap regression loss 𝐿 𝐻 can be defined as:EQUATIONHere, 𝑁 represents the number of landmarks. 𝑊 and 𝐻 represent the width and height of the output heatmap. ℎ 𝑖 𝑗𝑘 and ℎ * 𝑖 𝑗𝑘 represent the predicted and ground truth probability score value within the corresponding region of the heatmap, respectively.Due to the potential decrease in accuracy caused by smaller-sized heatmaps, we introduce the offset regression module. Each output offset map represents the offset between the precise position of the landmark and the approximate position obtained from the heatmap. The offset regression loss 𝐿 𝑂 can be defined as:EQUATIONHere, 𝑜 𝑖 𝑗𝑘𝑝 and 𝑜 * 𝑖 𝑗𝑘𝑝 represent the predicted and ground truth offset value within the corresponding region of the heatmap, respectively.To further enhance the accuracy of heatmap regression results, we introduce the neighbor regression module. During training, the neighbor regression module is utilized to predict the offset maps for 𝐵 neighbor landmarks of the current landmark. The neighbor regression loss 𝐿 𝑁 can be defined as:EQUATIONHere, 𝑛 𝑖 𝑗𝑘𝑝𝑏 and 𝑛 * 𝑖 𝑗𝑘𝑝𝑏 represent the predicted and ground truth offset distance between the current landmark and its neighbor landmarks, respectively.The total loss function of multi-branch heatmap regression module 𝐿 𝑡𝑜𝑡𝑎𝑙 consists of three components: heatmap regression loss 𝐿 𝐻 , offset regression loss 𝐿 𝑂 , and neighbor regression loss 𝐿 𝑁 :EQUATIONWhere 𝛼 𝐻 , 𝛼 𝑂 , 𝛼 𝑁 denote the balancing coefficients of three branches, respectively. 
experiments: 
 The experiments are conducted on our proposed dataset and the WFLW dataset. We compare the results of two training methods. Firstly, we train the model solely on FLSC dataset, referred to as the retraining model. Secondly, we train the model on the human dataset and subsequently fine-tune the pre-trained model using FLSC dataset, referred to as the fine-tuning model. We conduct tests for both methods on our dataset.For evaluation purposes, we utilize the normalized mean error (NME). The NME is calculated as the average Euclidean distance between the predicted locations of facial landmarks 𝑝 𝑖 𝑗 and their corresponding ground-truth annotations 𝑝 * 𝑖 𝑗 .𝑁 𝑀𝐸 = 1 𝑁 𝐿 𝑁 ∑︁ 𝑖=1 𝐿 ∑︁ 𝑗=1 |𝑝 𝑖 𝑗 -𝑝 * 𝑖 𝑗 | 𝑑 (6)where 𝑁 is the total number of images in the testing set, 𝐿 is the number of landmarks, and 𝑑 is the chosen normalization factor. In our study, we employ the "inter-ocular" normalization factor, which corresponds to the distance between the outer corners of the two eyes [4, 19] .Other Style We also export FR and AUC as evaluation metrics. The thresholds for these two metrics are both set to 0.1.In our method, the initial images are cropped and resized to a fixed size, i.e. 256 × 256, according to the annotated bounding boxes. Prior to training, we calculate the average facial landmark coordinates based on all images in the dataset. This allows us to select the 𝐵 closest landmarks as neighbors for each landmark. The heatmap output of the regression network is set to 16 × 16. In the neighbor regression module, the value of 𝐵, representing the number of neighbor landmarks, is set to 10. During training, the total number of epochs is set to 60. The initial learning rate is set to 1e-4, with learning rate decay applied at epochs 30 and 50. The batch size is set to 16. The values of 𝛼 𝐻 , 𝛼 𝑂 , and 𝛼 𝑁 are set to 1, 0.1, and 0.1, respectively.We compare our approach with open-sourced state-of-the-art methods 1 . We trained these models on the FLSC training set. Additionally, we trained separate models using retraining method and fine-tuning method. The performance comparisons of these methods on FLSC test set are shown in Table 2 .It is shown that our method achieve a slightly better result compared to existing landmark detection methods when applied to stylized faces. Moreover, within the current dataset, the retraining approach produces more precise results compared to the fine-tuning method.We also conducted a comparison between our model and existing stylized facial landmark detection models. The performance comparisons of these models on FLSC dataset are presented in Table 3 . It indicate that our model outperforms the currently available models in detecting landmarks on stylized faces.To demonstrate the effectiveness of our approach and the necessity of our dataset, we conduct three visualized comparisons. Backbone NME (%) FR 10% AUC 10% SBR [10] VGG-16 2 : Comparision with the state-of-the-art human facial landmark detection models on FLSC. These models are trained on FLSC training set. We export NME score, FR and AUC as evaluation metrics. The thresholds for the latter two are set to 0.1.Backbone NME (%) FR 10% AUC 10% dlib [ 3 : Comparision with the state-of-the-art stylized facial landmark detection models on FLSC. We export NME score, FR and AUC as evaluation metrics. The thresholds for the latter two are set to 0.1.Comparison with models trained on human face dataset. We conduct a comparison between models trained on the human landmark dataset(WFLW) and our stylized landmark dataset(FLSC). The validation data consists of characters with the style in our dataset and other styles outside the dataset. The comparative results are presented in Figure 3 . Notably, the model that is trained on real human datasets yields unsatisfactory results, while the retrained models exhibit significantly improved accuracy.Importantly, despite our dataset only consisting of limited styles of characters, the model trained on this dataset demonstrates superior performance when applied to characters with other styles. Therefore, we assert that our dataset can adequately provide models with the features of stylized characters.Comparison with existing stylized detectors. We compare our detector with existing stylized landmark detectors in Figure 4 . These landmark detectors produce 68 landmarks. To facilitate comparison, we convert the 98 landmarks generated by our model into 68 landmarks. The result indicates that existing detectors cannot provide accurate results, while the performance of our detector remarkably surpasses current results, especially in the region of the eyes, mouth, and cheek.Comparison with existing landmark detection methods. We compare our method with existing landmarks detection methods in Figure 5 . All methods are trained on our stylized landmark dataset. The result shows that our methods do achieve the best result in this task.Based on these visualizations and the quantitative results above, we believe that our method can perform well enough for stylized landmark detection, and our dataset holds great potential to assist researchers in achieving better results in related tasks. To better illustrate the performance of our detector, we provide a video that contains a 20-second stylized video and its detection result.  
applications: 
 In this section, we aim to discuss the practical applications of our work. Based on our dataset, the most fundamental application involves training a landmark detector for images featuring stylized characters. Building upon this foundation, we then categorize the subsequent applications into two main groups:The first category of the application involves leveraging landmarks as a bridge variable. On one end, we have 3D characterrelated data including cartoon videos and controller values. Through the utilization of the detector trained on our dataset, we can establish a mapping between landmarks and 3D character-related data. On the other end, we encounter diverse data or methods capable of generating landmarks, such as real human faces or results derived from generative models using text or audio. Using the mapping generated before, we can now establish a connection between these 3D character-related data and this landmark-oriented work.The second category involves transferring tasks that currently rely on landmarks to a new domain. These tasks are typically performed within the context of human faces, where landmarks serve as pivotal intermediary variables. With the aid of our detector, we can extend these tasks to the domain of stylized characters.Here we will provide two illustrative examples to demonstrate the application of our detector in these two categories.As depicted in Figure 6 , landmarks play a crucial role in bridging the gap between stylized characters and other works that involve landmark generation.Introduction. In certain cases, when we require 3D stylized characters to exhibit various facial expressions, we employ controller values to control their facial movements. Unlike other control methods such as blendshapes, this form of control enables direct manipulation of specific facial skeletal structures on the character. However, little research has been conducted on converting 2D stylized landmarks into controller values. Hence, it becomes necessary to utilize our dataset to train a mapping between stylized landmarks and controller values.The field of audio-driven talking heads has gained considerable attention, with several works employing landmarks as an intermediate output, such as Makeittalk [43] and MEAD [36] . The Makeittalk approach accepts images of a specific character as input and, guided Here, we present an example of how our dataset and method help to train a mapping between landmarks and controller values, further enabling a framework from audio to 3D character animation.Method. The first step in this process involves establishing a mapping between landmarks and controller values. Here we select an open-source character 'Mery', along with a foundational dataset containing its controller values. These controller values represent various expressions and mouth shapes. To construct the mapping dataset, we begin by rendering 2D images using Maya. Subsequently, we employ our fine-tuned model to extract landmarks from these images. The extracted landmarks are then normalized to ensure consistency in scale and position. With a one-to-one corresponding dataset between landmarks and controller values, we can train a Multilayer Perceptron (MLP) to map landmarks to their corresponding controller values.Next, we need to modify the work Makeittalk [43] to obtain the desired landmarks. The original approach utilizes AdaptiveWingLoss [37] to obtain the initial landmarks. Here we substitute this component with our detector and subject the output landmarks to the same normalization procedure. These normalized landmarks are then fed into the mapping model to generate the controller values. These results are then smoothed and integrated into Maya to animate the character.Result. For the mapping Model, we use MSE and failing rate to measure the accuracy. For those whose MSE loss is larger than 0.06, we define it as a failed sample. For our model, we achieve the following result: 𝑀𝑆𝐸 : 0.0462, 𝐹𝑅 : 0.195We present the comprehensive framework and results in Figure 7 . It is crucial to note that the original "Make it talk" framework was trained on human talking videos, which introduces inherent inaccuracies and may result in unnatural expressions within the stylized character domain. Hence, we showcase it merely as an example of a potential application of our dataset. Future endeavors, such as retraining the landmark generation model or incorporating transfer learning techniques, hold promise for improving the results and achieving more natural and realistic character expressions.Currently, many applications are based on the localization of facial landmarks. As depicted in Figure 6 , Our landmark detector allows for the extension of these applications from real human faces to stylized faces. Here, we provide an example for better understanding.Introduction. 3D face reconstruction is currently a prominent research field, aiming to transform 2D facial images into 3D meshes. In this context, the availability of facial images and precise facial landmark positions is crucial. Accurate localization of facial landmark positions significantly impacts the effectiveness of 3D reconstruction. However, the absence of a landmark detector specifically designed for stylized characters' faces poses a challenge for the 3D facial reconstruction of stylized characters.Here, we propose an example of how our dataset and method help to transfer the domain of this task from real humans to stylized characters.Method. A high-performing stylized characters' facial landmark detector can be trained on our FLSC dataset. For a 2D stylized face image, we first determine the approximate bounding box position using the facial landmark positions, followed by cropping and alignment of the facial region. Subsequently, the aligned facial image and its corresponding landmark positions are jointly fed into the 3D reconstruction model. Here we select Deep3DFace [7] as the model for 3D reconstruction.Result. Our model and results are presented in Figure 8 . Due to the lack of 3D meshes for stylized characters suitable for training Deep3DFace, we utilized a model trained on human faces. While the reconstructed 3D results exhibit relatively precise details, the stylistic resemblance closely aligns with that of human faces. Future work can build upon this by extending efforts to establish 3D stylized datasets for model training. 
conclusion: 
 Facial landmark detection involves identifying specific points on a face. However, the realm of stylized characters' facial landmarks remains relatively unexplored and presents unique challenges due to differences in facial structures and artistic styles. To address these Based on this dataset, we present StylizedFacePoint, a novel landmark detection framework leveraging a stacked hourglass network. After each stage of the hourglass network, we incorporate offset regression module and neighbor regression module to refine the precision of heatmap regression outcomes. Compared to state-ofthe-art methods, StylizedFacePoint demonstrates higher accuracy in landmark detection on stylized face images.Furthermore, we show potential applications of stylized characters' facial landmark detection. The first category involves using landmarks as bridge variables, and the second category explores domain transfer for tasks reliant on facial landmarks. For each category, we provide one detailed example as an illustration.In conclusion, our dataset and methods pave the way for face analysis of stylized characters, enabling further landmark-related work in this field.  
 
 Appendix: Some methods, including PFLD[14] and SPICA[26], use additional pose data, therefore can't be evaluated on our dataset. 


Decision: This paper proposes a facial landmark detection model designed for stylized animated characters. The main contribution is the proposed dataset (FLSC), which is composed of more than four thousand faces selected from 3D cartoons, and a deep learning based method (StylizedFacePoint), trained specifically for landmark detection on stylized animated characters. The Reviewers did not find an agreement on the paper. Two of them tended toward accepting the paper and found the dataset an interesting contribution. In my views, according to the reviews, the reasons for accepting the paper are prevailing on those for rejecting it. So my suggestion is to have the paper accepted as a poster.

Reviews: review: paper_summary: paper_summary: This paper proposes a facial landmark detection model designed for stylized animated characters. When compared to human faces, stylized characters can present unnatural features and proportions which are specific for each animation style, making models trained on real human faces unsuitable for the task.

Their main contributions are their newly proposed dataset (FLSC), which is composed of more than four thousand faces selected from 3D cartoons, with 98 landmarks per face, and a deep learning based method (StylizedFacePoint), trained specifically for landmark detection on stylized animated characters.

The proposed model is a stack of Hourglass Networks that generates several types of heatmaps, from which the x and y landmark positions are extracted. Their main contribution is the combination of a heatmap, offset and neighbour regression losses.

The authors evaluated their StylizedFacePoint method against 6 state-of-the-art methods for human facial landmark detection, and three methods for 2D animated cartoons and mangas, showing that the StylizedFacePoint trained (and not only fine-tune) on the FLSC dataset outperforms the other models.
strength_weakness: strength_weakness: The newly proposed FLSC dataset, which is the first comprising 2D landmarks from 3D animated movies, and its potential applications for 3D reconstruction and generation of facial movements from audio for animated characters.
limitations: limitations: In the section 2.1, the authors present a list of facial landmarks datasets, including real human faces, and 2D cartoon and manga faces in Table 1. However, only the datasets with human faces are described in the text, even though the other cartoon and manga datasets are closer to the task at hand. It is also important to clarify the differences between 2D cartoon, manga and artistic faces.

The authors should make it clear that the proposed dataset contains 2D landmarks. By looking at Table 1, it gives the idea that 3D landmarks were extracted.

A more detailed description of the dataset is missing. What is the proportion of multi-faces, non-frontal and frontal faces? In the test set, what is the proportion of samples obtained from styles not present in the training set? Moreover, in the "Landmarks Model" subsection, the authors should precise that they extracted 2D landmarks. 

As mentioned in Section 5.2, the coefficients for the heatmap, offset and neighbour regression losses are fixed to 1, 0.1 and 0.1. How were those values selected?

A few minor suggestions :

Line 111-112: "...train samples..." -> "...training samples..."

Line 305: "Subsequently, We..." -> "Subsequently, we..."

Line 308-310: "Neutral, Sad, Surprise, Happy, Angry, Disgust, and Afraid" -> "Neutral, Sadness, Surprise, Happiness, Disgust and Fear".

Line 336: "normalized heatmap..." -> "normalized heatmaps"
paper_summary: paper_summary: StylizedFacePoint is a novel landmark identification framework that uses a stacked hourglass network. Following each level of the hourglass network, we add the offset regression module and the neighbor regression module to improve the precision of heatmap regression results. When detecting landmarks on stylized face photos, StylizedFacePoint outperforms cutting-edge approaches.
strength_weakness: strength_weakness: The proposed study addresses a significant gap in the field of facial landmark detection by introducing the Facial Landmark Dataset for Stylized Characters (FLSC), a comprehensive dataset specifically designed to analyze facial features in stylized characters. This dataset, consisting of a substantial number of images and faces from cartoon video clips, offers a valuable resource for researchers to explore and develop methods tailored to stylized character analysis.
limitations: limitations: One notable limitation pertains to the scalability and diversity of the FLSC dataset. Although the dataset comprises a substantial number of images and faces from various cartoon video clips, its coverage may not fully encapsulate the breadth of stylized character styles encountered in real-world scenarios. Therefore, the generalizability of the proposed method to a broader range of stylized character designs could be further explored and validated through additional data collection efforts or augmentation techniques.

Moreover, while the StylizedFacePoint method demonstrates superior performance, its computational efficiency and robustness to variations in illumination, pose, and facial expression across different character styles require further investigation and optimization. Conducting rigorous experiments and benchmarking against diverse datasets would provide valuable insights into the method's limitations and areas for improvement.
paper_summary: paper_summary: This work tackles the problem of facial landmark detection on 3D stylised characters. In particular, it introduces the first dataset that contains facial landmarks for 3D characters, termed FLSC. It further introduces an end-to-end trainable model, StylizedFacePoint, to conduct landmark detections on stylised characters. Experiments on the introduced FLSC dataset demonstrate the efficacy of the introduced StylizedFacePoint.
strength_weakness: strength_weakness: * It is interesting and meaningful to address the problem of facial landmark detection on 3D stylised characters.
* A 3D stylised character dataset with facial landmarks and facial expressions will benefit the community.
limitations: limitations: * Lack of technical contributions. The introduced StylizedFacePoint appears to be a simple utilisation of the Hourglass Network. It would be better to detail which parts of the model are tailored for stylized characters. 

* Lack of comparisons on existing benchmarks. To thoroughly verify the effectiveness of the introduced model, it would be better to also compare it with other existing models on other existing benchmarks, e.g., on the Sindel et al. [29]

* Lack of ablation studies. The overall training objective (Eq. 5) comprises three loss terms. It would be better to conduct ablation studies to verify the main function of each loss term.

* Lack of discussions with more recent work. STAR [ref1] also leverages the hourglass network to achieve facial landmark detection; it sets the state-of-the-art performance, and it is open source. It might be better to compare with STAR to demonstrate the superiority of the introduced StylizedFacePoint


### References:

[ref1] Zhou, Zhenglin, et al. "Star loss: Reducing semantic ambiguity in facial landmark detection." CVPR 2023.