Title: SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models
Venue: NeurIPS 2024
Accepted: yes
Keywords: Makeup transfer

self-supervised learning

diffusion models
Abstract: This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image.  Due to the absence of paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. Additionally, different makeup styles generally have varying effects on the person face, but existing methods struggle to deal with this diversity. To address these issues, we propose a novel Self-supervised Hierarchical Makeup Transfer (SHMT) method via latent diffusion models. Following a "decoupling-and-reconstruction" paradigm, SHMT works in a self-supervised manner, freeing itself from the misguidance of imprecise pseudo-paired data. Furthermore, to accommodate a variety of makeup styles, hierarchical texture details are decomposed via a Laplacian pyramid and selectively introduced to the content representation. Finally, we design a novel Iterative Dual Alignment (IDA) module that dynamically adjusts the injection condition of the diffusion model, allowing the alignment errors caused by the domain gap between content and makeup representations to be corrected. Extensive quantitative and qualitative analyses demonstrate the effectiveness of our method. Our code is available at https://github.com/Snowfallingplum/SHMT.

SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models

Abstract

This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence of paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. Additionally, different makeup styles generally have varying effects on the person face, but existing methods struggle to deal with this diversity. To address these issues, we propose a novel Self-supervised Hierarchical Makeup Transfer (SHMT) method via latent diffusion models. Following a "decoupling-and-reconstruction" paradigm, SHMT works in a self-supervised manner, freeing itself from the misguidance of imprecise pseudo-paired data. Furthermore, to accommodate a variety of makeup styles, hierarchical texture details are decomposed via a Laplacian pyramid and selectively introduced to the content representation. Finally, we design a novel Iterative Dual Alignment (IDA) module that dynamically adjusts the injection condition of the diffusion model, allowing the alignment errors caused by the domain gap between content and makeup representations to be corrected. Extensive quantitative and qualitative analyses demonstrate the effectiveness of our method. Our code is available at https://github.com/Snowfallingplum/SHMT .

1 Introduction

Recently, makeup transfer has become a popular application in social media and the virtual world. With its significant economic potential in e-commerce and entertainment, this technique is attracting widespread attention from the computer vision and artificial intelligence communities. Given a pair of source and reference face images, makeup transfer involves simultaneously focusing on the realism of the transferred result, the content preservation of the source image, and the makeup fidelity of the reference image. Although previous approaches [21, 3, 11, 4, 17, 24, 7, 29, 27, 16, 26, 41, 36, 47, 46] have made significant advances in image realism and content preservation , the challenge of achieving high-fidelity transfer of various makeup styles still remains unsolved.

The difficulties of makeup transfer mainly stem from two aspects. On the one hand, makeup transfer is essentially an unsupervised task, which means that there are no real transferred images that can be used as labeled targets for model training. To address this issue, previous methods typically synthesize a "pseudo" ground truth from each input source-reference image pair, as an alternative supervision

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signal. However, as shown in Figure 1(a), current pseudo-paired data synthesis techniques, including histogram matching [21, 29] and geometric distortion [11, 41, 36], fail to produce desirable outcomes. The reason is that histogram-matching-based methods often ignore the spatial properties of makeup styles, thus generating over-smoothed targets that lose most makeup details. And since the warping process in geometric-distortion-based methods relies solely on the shape information (e.g., facial landmarks) of input images, their pseudo targets usually contain undesired artifacts. Consequently, these sub-optimal pseudo-paired data will inevitably misguide the model training. This eventually results in the fact that most existing methods [21, 17, 24, 7, 41, 36, 47] generally exhibit low makeup fidelity, especially for those images with complex makeup details.

On the other hand, the diversity of different makeup styles can also lead to ambiguity in preserving source contents. In practice, makeup styles can range from natural, barely-there looks to elaborate and dramatic ones, each having a different impact on the person face. As shown in Figure 1(b), the content details of a source face (e.g., freckles and eyelashes) usually should be preserved in a simple makeup style, while they may be obscured in some complex ones due to the heavy use of cosmetics. An ideal model should be flexible enough to preserve or discard those source content details according to user preferences. However, all the previous approaches overlook this requirement.

To address the aforementioned dilemmas, we propose a novel Self-supervised Hierarchical Makeup Transfer (SHMT) method, which is built on the recent latent diffusion models [31]. For the first problem, considering the unsupervised nature of makeup transfer, we develop a self-supervised learning strategy following a "decoupling-and-reconstruction" paradigm. Specifically, given a face image, SHMT first extracts its content and makeup representations, and then simulates the makeup transfer procedure by reconstructing the original input from these decoupled information. In SHMT, the content representation of a face image includes its 3D shape and texture details, while the associated makeup representation is captured by destroying the content information from the input image through using random spatial transformations. In this way, SHMT works in a self-supervised manner, thus eliminating the misguidance of pseudo-paired data. To address the second issue, we

introduce a Laplacian pyramid [2] to hierarchically decompose the texture information in input image, allowing SHMT to flexibly control the preservation or discard of these content details for various makeup styles, as illustrated in Figure 2. Additionally, we propose an Iterative Dual Alignment (IDA) module in conjunction with the stepwise denoising property of diffusion models. In each denoising step, IDA utilizes the intermediate result to dynamically adjust the injection condition, which will help to correct the alignment errors caused by the domain gaps between the content and makeup representations. Our main contributions can be summarized as follows:

Wepropose a novel makeup transfer method, named SHMT, which employs a self-supervised learning strategy for model training, thus getting rid of the misleading pseudo-pairing data adopted by previous methods.

A Laplacian pyramid is introduced to hierarchically characterize the texture information, enabling SHMT to flexibly process these content details for various makeup styles.

A new Iterative Dual Alignment (IDA) module is proposed, which dynamically adjusts the injection condition in each denoising step, such that the alignment errors caused by the domain gaps between the content and makeup representations can be corrected.

Extensive qualitative and quantitative results indicate that SHMT outperforms other stateof-the-art makeup transfer methods. And additionally, the ablation studies demonstrate the robustness and the generalization ability of our SHMT method.

2 Related Works

2.1 Makeup Transfer

Over the past decade, makeup transfer [38, 12, 19] has gained increasing attention in the field of computer vision. BeautyGAN [21] designs a histogram matching loss and a dual input/output GAN [10] to simultaneously perform makeup transfer and removal. PairedCycleGAN [3] trains additional style discriminators to measure the local makeup similarity between the results and reference images. BeautyGlow [4] decomposes the latent vectors of face images derived from the Glow [18] framework into makeup and nonmakeup latent vectors. To address misaligned head poses and facial expressions, PSGAN [17, 24] utilizes an attention mechanism [40] to adaptively deform the makeup feature maps based on source images, while SCGAN [7] encodes component-wise makeup regions into spatiallyinvariant style codes. RamGAN [45] and SpMT [54] explore local attention to eliminate potential associations between different makeup components. Considering that histogram matching discards the spatial properties of the makeup styles, FAT [41] and SSAT [36, 37] design a pseudo-paired data synthesis based on geometric distortion. EleGANt [47] proposes a more effective pseudo-paired data by assigning varying weights to above two synthesis methods for performance improvement. For complex makeup styles, LAND [11] leverages multiple overlapping local makeup style discriminators to focus on the high-frequency makeup details. Additionally, CPM [29] applies a segmentation model to predict the mask of the makeup pattern. This segmented pattern is then pasted into semantically identical locations using UV [8] space.

Due to the unsupervised nature of makeup transfer, most of the above methods synthesize pseudopaired data to guide model training. In this strategy, the quality of these pseudo-paired data is critical, leading many works [3, 41, 36, 29, 47] to strive for better synthesis techniques. With the help of the unprecedented generative capabilities of both GPT-4V and Stable Diffusion, a concurrent work Stable-Makeup [52] produces higher quality pseudo-paired data, thereby improving the performance of makeup transfer. Unlike these methods, our approach works in a self-supervised manner and eliminates the need for cumbersome pseudo-paired data synthesis.

2.2 Diffusion Models

Diffusion models generate realistic images through an iterative inverse denoising process. Recently, as competitors to generative adversarial networks [10], diffusion models have shown significant progress in numerous generative tasks, including Text-to-Image (T2I) generation [30, 33, 31], controllable editing [28, 51, 48], and subject-driven [9, 32, 5] or human-centric [22, 44, 43] synthesis. DDPM [15] proves the feasibility of recovering realistic images from random Gaussian noise. DDIM [35] enables fast and deterministic inference by transforming the sampling process into a non-Markovian

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process. To reduce computational complexity while retaining high quality and flexibility, Latent Diffusion Models (LDM) [31] apply diffusion model training in the latent space of powerful pretrained autoencoders. With training on large datasets, both Imagen [33] and Stable Diffusio [31] elevate T2I synthesis to an unprecedented level.

Inspired by the above approach, we delve into the makeup transfer task based on diffusion models. Compared to previous GAN-based makeup transfer methods, our approach eliminates the need for adversarial training and tedious loss function design, while delivering enhanced performance.

3 Our Methodology

3.1 Preliminary

Our method is developed from Latent Diffusion Model (LDM) [31], which performs the diffusion process in the latent space to reduce the computational complexity of the model. In particular, LDM comprises three key components: an image encoder E , a decoder D , and an UNet denoiser ϵ θ . Firstly, the encoder E compresses an image I from the pixel space to a low-dimensional latent space z 0 = E ( I ) , while the decoder D efforts to reconstruct the original image I from the latent variable z 0 (e.g. D ( z 0 ) = x 0 ≈ I ). Then, a UNet denoiser ϵ θ is trained to predict the applied noise in the latent space. The optimization process can be defined as the following formulation:

where z t is the noisy latent variable at the timestep t , c is the conditioned signal, ϵ is randomly sampled from the standard Gaussian distribution, and T is the defined maximum timestep.

During inference, z T is sampled from a random Gaussian distribution. The UNet denoiser ϵ θ then iteratively predicts the noise in the latent space at each timestep t and restores z 0 via a sampling process (e.g. DDPM [15] or DDIM[35]). Finally, z 0 is reconstructed by the decoder D to obtain the generated image. See [31] for more details.

3.2 Overview

The goal of makeup transfer is to generate a new facial image that preserves the content information (e.g., background, facial structure, pose, expression) of a source image while applying the makeup style of a reference image. To achieve this, we propose SHMT, with its framework outlined in Figure

3. In SHMT, we craft a self-supervised strategy for model training in Section 3.3 and design an Iterative Dual Alignment (IDA) module to correct alignment errors in Section 3.4. The training and inference process of SHMT is presented in Section 3.5.

3.3 Self-supervised Strategy

Following a "decoupling-and-reconstruction" paradigm, we craft a self-supervised strategy for makeup transfer. The main idea is to separate content and makeup representations from a facial image, and then reconstruct the original image from these components.

Foreground and background segmentation. Makeup transfer is a localized modification task confined to the facial area. As such, a pre-trained face parsing model [49] is used to segment the foreground and background areas from the original image I . In makeup transfer, the background area I bg (which includes hair and clothes) is a known component. Hence, the background area I bg is input to the latent diffusion model along with the noisy image I t . The goal of the model is to inpaint the unknown facial area, using subsequent content and makeup representations as conditions.

Makeup Representation. In our approach, the makeup representation is derived by destroying the image's content information. Specifically, we apply a sequence of spatial transformations to the foreground image I fg . These transformations include random cropping, rotation, and elastic distortion to create variation. As illustrated in Figure 3, the distorted foreground image I m , while losing the majority of the content information, retains the makeup information well. In addition, these transformations effectively mimic semantic misalignment scenarios and facilitate the robustness of the model to poses and expressions [17, 7].

Content Representation. Inspired by physical face modeling [12, 19, 20], the content representation of a face is simplified into two main components: face shape and texture details.

Face Shape : The face shape, which determines the facial structure, head pose, and expression, is a crucial component to preserve during makeup transfer. In this context, a typical 3D face reconstruction model, known as 3DDFA-V2 [13], is employed to extract the face shape I 3 d from the original image I . To match the resolution of the latent space of LDM, we perform a pixel unshuffle operation [34] to downsample I 3 d .

Texture Details : Texture details are incorporated to complement the excessively smooth face shape. Considering the ambiguity of texture detail preservation, the Laplace pyramid [2] is introduced to build hierarchical texture details . To prevent color disturbances, the foreground image I fg is first converted to a grayscale image ˆ I fg of h × w pixels. Then we downsample it by applying a fixed Gaussian kernel to produce a low-pass prediction l 1 ∈ R h 2 × w 2 . The high-frequency component h 0 , which is treated as texture details, can be calculated as h 0 = ˆ I fg -ˆ l 1 , where ˆ l 1 is upsampled from l 1 . By replacing ˆ I fg with l 1 and repeating these operations, we obtain a series of hierarchical texture details [ h 0 , h 1 , · · · , h L ] , where L refers to the decomposition level of the Laplace pyramid. These texture details, whose resolution gradually halves and ranges from fine to coarse, are illustrated in Figure 3. If the resolution of the texture details exceeds that of the latent space, we downsample it using a pixel unshuffle operation [34]. Otherwise, the bilinear interpolation is used for upsampling.

Note that only one texture detail h i is concatenated to the face shape I 3 d as a complete content representation. When h i is a fine texture detail, our model only needs to distill the low-frequency makeup information from the makeup representation to reconstruct the image. This is suitable for simple makeup styles. When h i is a coarse texture detail, our model must also distill the highfrequency makeup information from the makeup representation to ensure the recovery of the image. This is suitable for complex makeup styles.

3.4 Iterative Dual Alignment

To reconstruct the original image, spatial attention [40] is utilized to semantically align distorted makeup representation with content representation. At each timestep, we constructs a pixel-wise correlation matrix M by calculating the cosine similarity as:

where f c = E c ( I 3 d , h i ) , f m = E m ( E ( I m )) denote the semantic features extracted by encoders E c and E m . f ( i ) represents the feature vector of the i -th pixel in f and M ( i, j ) indicates the element at the ( i, j ) -th location of M . We consider the correlation matrix M as a deformation mapping function, and use it to spatially deform the feature maps z m = E ( I m ) of makeup representations:

where Softmax ( · ) denotes a softmax computation along the column dimension, which normalizes the element values in each row of M, and τ > 0 is a temperature parameter. Theoretically, the deformed feature maps z ′ m is semantically aligned with the content representation. However, due to the domain gap between content and makeup representations, we find that alignment errors occur frequently in z ′ m . Considering the property that noisy intermediate result I t is gradually moving closer to the real image domain (e.g., the makeup representation domain), we propose a Iterative Dual Alignment (IDA) module to address the above issue. At each timestep, it calculates an extra alignment prediction z ′′ m between the noisy intermediate result ˆ I t and makeup representation I m to dynamically correct the previous alignment prediction z ′ m . ˆ I t is the result of I t removing the background area. Since the noise degree of ˆ I t is determined by timestep t , a MLP module predicts the percentage w of two alignment predictions from timestep t :

where w = MLP ( t ) and the MLP module consists of two fully connected layers and ends with a Sigmoid activation layer. Finally, the mixed prediction ˆ z m is concatenated with the content representation and injected into the denoiser's encoder through a projection module consisting of 1 × 1 convolution. In addition to correct alignment errors, IDA has two other advantages. First, because ˆ z m is semantically aligned with the content representation, IDA can control the makeup style in a spatially-aware manner. Corresponding results are displayed in the supplementary material. Second, IDA is relatively lightweight, with only approximately 11M parameters.

3.5 Training and Inference

During training, the parameters of the pre-trained autoencoder are fixed, the U-net denoiser ϵ θ and IDA module are jointly optimized from scratch under the original objective function in Equation 1. At inference, the model receives the background area I bg and content representation ( I 3 d and h i ) from the source image, as well as the undeformed makeup representation I m from the reference image, to generate the makeup transfer result.

4 Experiments

4.1 Experimental settings

Datasets. Following [21, 17], we randomly select 90% of the images from the MT dataset [21] as training samples and the rest as test samples. In addition, Wild-MT [17] and LADN [11] datasets are also used to validate the performance and generalization capability of our model. The images in the Wild-MT dataset contain large pose and expression variations, and the LADN dataset collects a number of images with complex makeup styles.

Implementation Details. In our experiments, we discover that the autoencoder with a downsampling factor of 4 preserves texture details better than the one with a factor of 8. Therefore, the autoencoder with a downsampling factor of 4 is selected, and the SHMT model is trained at a resolution of 256 × 256. The specific structure of the UNet denoiser ϵ θ remains the same as the LDM [31], with IDA module replacing the original conditional injection module. In Equation 3, τ is set to 100. We train the model with Adam optimizer, learning rate of 1e-6 and batch size of 16 on a single A100 GPU. Our model is trained for 250, 000 steps in about 5 days. For sampling, we utilize 50 steps of the DDIM sampler [35].

Evaluation Metrics. In order to comprehensively and objectively compare the different methods, we choose three evaluation metrics, FID , CLS and Key-sim . Following [24], FID [14] is calculated between reference images and transferred results to indicate image realism . The lower the FID , the

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better. Recently, the work [39] proves that the CLS token and the self-similarity of keys (abbreviated as Key-sim ) in DINO's [50] feature space can represent the appearance and structure of an image, respectively. Inspired by this, we compute the cosine similarity of the CLS token between reference images and transferred results to represent the makeup fidelity , and the cosine similarity of Key-sim between source images and transferred results to reflect the content preservation . The higher the CLS and Key-sim , the better.

Baselines. We choose seven state-of-the-art makeup transfer methods as baselines, including PSAGN [17], SCGAN [7], EleGANt [47], SSAT [36], LADN [11], CPM[29] and Stable-Makeup[52]. Among them, only Stable-Makeup is a diffusion-model-based method, while the others are GAN-based methods. And LADN, CPM and Stable-Makeup focus on complex makeup styles. In our experiments, the baseline results are derived from official publicly available code or pre-trained models.

4.2 Comparisons

Qualitative Results. The qualitative comparison with different GAN-based methods for simple and complex makeup styles are shown in Figure 4 and Figure 5, respectively. Although PSGAN, SCGAN, EleGANt, and SSAT preserve the content of the source image well, they have low fidelity for reference makeup, especially in complex makeup styles. In addition, they have a tendency to modify

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the background color, e.g., the second and fourth rows of Figure 5. LADN's results are accompanied by a large number of artifacts and content distortions. CPM performs relatively satisfactorily in complex makeup, but still fails to reproduce some high-frequency makeup details. Due to the UV space not including the forehead area, the CPM has a noticeable sense of pasting, and there are some artifacts along the facial contour. The results of diffusion-model-based methods are shown in Figure 6 and Figure 7. For simple makeup styles, the results of Stable-Makeup show a noticeable color shift when compared to the reference makeup styles. In addition, these results tend to alter the content information of the source image, including identity and expression. For complex makeup styles, the results of Stable-Makeup still show a significant loss of high-frequency makeup details. In contrast, our SHMT method can naturally and accurately reproduce various makeup styles on the source face by equipping it with different texture details.

Quantitative Results. In our experiments, we randomly selected 1000 source-reference image pairs from the test set of each dataset to calculate each evaluation metric. The quantitative results of the different methods are listed in Table 1. SHMTh 0 equipped with fine texture details h 0 obtains the highest value on Key-sim , indicating better content preservation. SHMTh 4 equipped with coarse texture details h 4 achieves the highest values on FID , CLS , suggesting greater image realism and makeup fidelity. This also demonstrates that there is a tradeoff between content preservation and makeup fidelity in the makeup transfer task.

User Study . We also perform a user study to evaluate the performance of different models. We randomly select 50 pairs of images with different types of makeup styles and generate the transferred

results using different methods. Then, 8 participants are asked to choose the most satisfactory result, considering image realism, content preservation, and makeup fidelity. To ensure a fair comparison, the transferred results are displayed simultaneously in a random order. The results of the user study are shown in Table 2.

4.3 Ablation Study

The effectiveness of hierarchical texture details. Figure 8 (a) illustrates the visual comparison of SHMT with varying texture details in handling both simple and complex makeup styles. SHMTh 0 tends to preserve high-frequency details of the source image, such as subtle expressions, single and double eyelids, eyelashes, and freckles. It is more suitable for simple makeup transfer. On the other hand, SHMTh 4 is more likely to transfer the high-frequency details from the reference image, making it more appropriate for complex makeup transfer. By staggering the timestep and employing a different model to predict the noise, i.e., using SHMTh 0 for [T,t] and SHMTh 4 for (t,0), we can easily achieve a seamless interpolation between the two results.

The effectiveness of IDA. To verify the effectiveness of IDA, we remove the additional alignment prediction z ′′ m and replace ˆ z m with z ′ m as an injection condition for the denoiser. We retrain the SHMTh 4 model, and the alignment visualizations and transfrred results are shown in Figure 8 (b). As seen, the proposed IDA module effectively corrects alignment errors and enhances makeup fidelity. The quantitative results are displayed in Table 3. In Figure 8 (c), the trend of percentage w over time is investigated. As timestep t becomes smaller, the domain gap between the noisy intermediate result ˆ I t and makeup representation I m gets smaller, and w increases indicating that the model favors more the alignment prediction of these two. In addition, w of SHMTh 4 is significantly higher than that of SHMTh 0 at each timestep t , which we attribute to the fact that transfering high-frequency texture details requires more precision in semantic alignment.

Robustness and Generalization. Further, as shown in Figure 9 (a), calculating the semantic alignment between the source and reference images makes our model insensitive to age, gender, pose and expression variations. To evaluate the generalization ability, we collect some sketch and anime examples which have a significant domain gap with the training samples and have never been encountered by the model before. The results are displayed in Figure 9 (b).

(a) The effectiveness of hierarchical texture details

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4.4 Limitations

The limitations of our method can be summarized in two aspects. First, our proposed model relies on the prior knowledge of the pre-trained models (face parsing and 3D reconstruction), and the stability of our model suffers when their output is inaccurate. More results and discussion are provided in the supplementary material. Second, compared to previous GAN-based approaches, our model has more parameters and requires more computational resources during inference, taking several seconds to generate an image. Recent accelerated sampling techniques [25, 53, 23] may be able to alleviate this limitation to some extent.

5 Conclusion

In this paper, we propose a Self-supervised Hierarchical Makeup Transfer (SHMT) method. It employs a self-supervised strategy for model training, freeing itself from the misguidance of pseudopaired data employed by previous methods. Benefiting from hierarchical texture details, SHMT can flexibly control the preservation or discarding of texture details, making it adaptable to various makeup styles. In addition, the proposed IDA module is capable of effectively correcting alignment errors and thus enhancing makeup fidelity. Both quantitative and qualitative analyses have demonstrated the effectiveness of our SHMT method.

Acknowledgments

This work was in part supported by the National Key Research and Development Program of China (Grant No. 2022ZD0160604), the National Natural Science Foundation of China (Grant No. 62176194), the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 62306219), the Key Research and Development Program of Hubei Province (Grant No. 2023BAB083), the Project of Sanya Yazhou Bay Science and Technology City (Grant No. SCKJJYRC-2022-76, SKJC-2022-PTDX-031), the Project of Sanya Science and Education Innovation Park of Wuhan University of Technology (Grant No. 2021KF0031), Alibaba Group through Alibaba Research Intern Program.

References

Agil Aghasanli, Dmitry Kangin, and Plamen Angelov. Interpretable-through-prototypes deepfake detection for diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision , pages 467-474, 2023.

Peter J Burt and Edward H Adelson. The laplacian pyramid as a compact image code. In Readings in computer vision , pages 671-679. Elsevier, 1987.

Huiwen Chang, Jingwan Lu, Fisher Yu, and Adam Finkelstein. Pairedcyclegan: Asymmetric style transfer for applying and removing makeup. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 40-48, 2018.

Hung-Jen Chen, Ka-Ming Hui, Szu-Yu Wang, Li-Wu Tsao, Hong-Han Shuai, and Wen-Huang Cheng. Beautyglow: On-demand makeup transfer framework with reversible generative network. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , pages 10042-10050, 2019.

Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. arXiv preprint arXiv:2307.09481 , 2023.

Riccardo Corvi, Davide Cozzolino, Giada Zingarini, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva. On the detection of synthetic images generated by diffusion models. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 1-5. IEEE, 2023.

Han Deng, Chu Han, Hongmin Cai, Guoqiang Han, and Shengfeng He. Spatially-invariant style-codes controlled makeup transfer. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , pages 6549-6557, 2021.

Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. Joint 3d face reconstruction and dense alignment with position map regression network. In Proceedings of the European conference on computer vision (ECCV) , pages 534-551, 2018.

Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 , 2022.

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems , 27, 2014.

Qiao Gu, Guanzhi Wang, Mang Tik Chiu, Yu-Wing Tai, and Chi-Keung Tang. Ladn: Local adversarial disentangling network for facial makeup and de-makeup. In Proceedings of the IEEE/CVF International conference on computer vision , pages 10481-10490, 2019.

Dong Guo and Terence Sim. Digital face makeup by example. In 2009 IEEE Conference on Computer Vision and Pattern Recognition , pages 73-79. IEEE, 2009.

Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, and Stan Z Li. Towards fast, accurate and stable 3d dense face alignment. In European Conference on Computer Vision , pages 152-168. Springer, 2020.

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems , 30, 2017.

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems , 33:6840-6851, 2020.

Zhikun Huang, Zhedong Zheng, Chenggang Yan, Hongtao Xie, Yaoqi Sun, Jianzhong Wang, and Jiyong Zhang. Real-world automatic makeup via identity preservation makeup net. In International Joint Conference on Artificial Intelligence . International Joint Conference on Artificial Intelligence, 2021.

Wentao Jiang, Si Liu, Chen Gao, Jie Cao, Ran He, Jiashi Feng, and Shuicheng Yan. Psgan: Pose and expression robust spatial-aware gan for customizable makeup transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5194-5202, 2020.

Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Advances in neural information processing systems , 31, 2018.

Chen Li, Kun Zhou, and Stephen Lin. Simulating makeup through physics-based manipulation of intrinsic image layers. In Proceedings of the IEEE Conference on computer vision and pattern recognition , pages 4621-4629, 2015.

Tianye Li, Timo Bolkart, Michael J Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph. , 36(6):194-1, 2017.

Tingting Li, Ruihe Qian, Chao Dong, Si Liu, Qiong Yan, Wenwu Zhu, and Liang Lin. Beautygan: Instancelevel facial makeup transfer with deep generative adversarial network. In Proceedings of the 26th ACM international conference on Multimedia , pages 645-653, 2018.

Chao Liang, Fan Ma, Linchao Zhu, Yingying Deng, and Yi Yang. Caphuman: Capture your moments in parallel universes. arXiv preprint arXiv:2402.00627 , 2024.

Shanchuan Lin, Anran Wang, and Xiao Yang. Sdxl-lightning: Progressive adversarial diffusion distillation. arXiv preprint arXiv:2402.13929 , 2024.

Si Liu, Wentao Jiang, Chen Gao, Ran He, Jiashi Feng, Bo Li, and Shuicheng Yan. Psgan++: Robust detail-preserving makeup transfer and removal. IEEE Transactions on Pattern Analysis and Machine Intelligence , 44(11):8538-8551, 2021.

Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095 , 2022.

Yueming Lyu, Jing Dong, Bo Peng, Wei Wang, and Tieniu Tan. Sogan: 3d-aware shadow and occlusion robust gan for makeup transfer. In Proceedings of the 29th ACM International conference on multimedia , pages 3601-3609, 2021.

Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley. Least squares generative adversarial networks. In Proceedings of the IEEE international conference on computer vision , pages 2794-2802, 2017.

Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. T2iadapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence , pages 4296-4304, 2024.

Thao Nguyen, Anh Tuan Tran, and Minh Hoai. Lipstick ain't enough: beyond color matching for inthe-wild makeup transfer. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , pages 13305-13314, 2021.

Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 , 1(2):3, 2022.

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10684-10695, 2022.

Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 22500-22510, 2023.

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-toimage diffusion models with deep language understanding. Advances in neural information processing systems , 35:36479-36494, 2022.

Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1874-1883, 2016.

Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 , 2020.

Zhaoyang Sun, Yaxiong Chen, and Shengwu Xiong. Ssat: A symmetric semantic-aware transformer network for makeup transfer and removal. In Proceedings of the AAAI Conference on artificial intelligence , pages 2325-2334, 2022.

Zhaoyang Sun, Yaxiong Chen, and Shengwu Xiong. Ssat++: A semantic-aware and versatile makeup transfer network with local color consistency constraint. IEEE Transactions on Neural Networks and Learning Systems , 2023.

Wai-Shun Tong, Chi-Keung Tang, Michael S Brown, and Ying-Qing Xu. Example-based cosmetic transfer. In 15th Pacific Conference on Computer Graphics and Applications (PG'07) , pages 211-218. IEEE, 2007.

Narek Tumanyan, Omer Bar-Tal, Shai Bagon, and Tali Dekel. Splicing vit features for semantic appearance transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10748-10757, 2022.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems , 30, 2017.

Zhaoyi Wan, Haoran Chen, Jie An, Wentao Jiang, Cong Yao, and Jiebo Luo. Facial attribute transformers for precise and robust makeup transfer. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 1717-1726, 2022.

Haofan Wang, Qixun Wang, Xu Bai, Zekui Qin, and Anthony Chen. Instantstyle: Free lunch towards style-preserving in text-to-image generation. arXiv preprint arXiv:2404.02733 , 2024.

Qinghe Wang, Xu Jia, Xiaomin Li, Taiqing Li, Liqian Ma, Yunzhi Zhuge, and Huchuan Lu. Stableidentity: Inserting anybody into anywhere at first sight. arXiv preprint arXiv:2401.15975 , 2024.

Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, and Anthony Chen. Instantid: Zero-shot identity-preserving generation in seconds. arXiv preprint arXiv:2401.07519 , 2024.

Jianfeng Xiang, Junliang Chen, Wenshuang Liu, Xianxu Hou, and Linlin Shen. Ramgan: region attentive morphing gan for region-level makeup transfer. In European Conference on Computer Vision , pages 719-735. Springer, 2022.

Qixin Yan, Chunle Guo, Jixin Zhao, Yuekun Dai, Chen Change Loy, and Chongyi Li. Beautyrec: Robust, efficient, and content-preserving makeup transfer. arXiv preprint arXiv:2212.05855 , 2022.

Chenyu Yang, Wanrong He, Yingqing Xu, and Yang Gao. Elegant: Exquisite and locally editable gan for makeup transfer. In European Conference on Computer Vision , pages 737-754. Springer, 2022.

Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.06721 , 2023.

Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. Bisenet: Bilateral segmentation network for real-time semantic segmentation. In Proceedings of the European conference on computer vision (ECCV) , pages 325-341, 2018.

Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 , 2022.

Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 3836-3847, 2023.

Yuxuan Zhang, Lifu Wei, Qing Zhang, Yiren Song, Jiaming Liu, Huaxia Li, Xu Tang, Yao Hu, and Haibo Zhao. Stable-makeup: When real-world makeup transfer meets diffusion model. arXiv preprint arXiv:2403.07764 , 2024.

Zhenyu Zhou, Defang Chen, Can Wang, and Chun Chen. Fast ode-based sampling for diffusion models in around 5 steps. arXiv preprint arXiv:2312.00094 , 2023.

Mingrui Zhu, Yun Yi, Nannan Wang, Xiaoyu Wang, and Xinbo Gao. Semi-parametric makeup transfer via semantic-aware correspondence. arXiv preprint arXiv:2203.02286 , 2022.

Appendix

A The Effectiveness of Hierarchical Texture Details

As the texture details go from fine to coarse, the high-frequency information of the original image provided in the content representation decreases. In order to reconstruct the original image, our model SHMT has to learn more high-frequency details from the makeup representation, thus adapting to more complex makeup styles. To verify this, we train the SHMT model equipped with different texture details separately, and the corresponding results are shown in Figure 10. From h 0 to h 4 , our model gradually shifts from preserving the texture details of the source image to transferring the texture details of the reference image, such as the skeleton makeup style in the third row. The quantitative results of the corresponding models on the LADN dataset are displayed in Table 4. As the metric CLS gradually increases, the metric Key-sim gradually decreases, which also indicates a trade-off between makeup fidelity and content preservation.

SHMT-

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B Comparison with InstantStyle

We also compare the proposed method with the recent style transfer method InstantStyle [42]. The qualitative results are shown in Figure 11. In InstantStyle [42], we fix the text prompt to "a woman, best quality, high quality", the number of samples is set to 4, and other parameter configurations follow the default settings. As seen, InstantStyle [42] captures the color and brushstroke style of the global image, not the makeup style of the face. Additionally, it does not effectively preserve the content information of the source image, indicating that the generalized style transfer method may not be suitable for the specific makeup transfer task.

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Prompt: a woman, best quality, high quality

SHMT

C Makeup Style Control

C.1 Global Makeup Interpolation

In our proposed method, the makeup information is decoupled from the input images and encoded into the feature maps ˆ z m . This allows us to interpolate the makeup styles between two different reference faces by linearly fusing their the feature maps ˆ z m , as follows:

Here ˆ z m 1 and ˆ z m 2 are alignment predictions of two different reference images, respectively. By adjusting the value of β from 0 to 1, SHMT can generate a series of transferred results. Their makeup styles will gradually change from that of one reference image y 1 to that of the other y 2 . Moreover, by assigning the source image as y 1 , we can control the degree of makeup transfer for a single reference input y 2 . The global makeup interpolation results are shown in Figure 12.

C.2 Local Makeup Interpolation

In SHMT, the alignment prediction ˆ z m is deformed through the spatial attention, so that it can be semantically aligned with the source image. Such spatial alignment enables SHMT to implement the makeup interpolation within different local facial areas, which can be formulated as follows:

where ⊗ denotes the Hadamard product and ˆ z m _ self denotes the alignment prediction by assigning the source image as a reference image. Mask area is a binary mask of the source image x , indicating the local areas to be makeup, which can be obtained by face parsing. Figure 13 visualizes the local makeup interpolation results within the areas around the lips and eyes, respectively, i.e., area ∈ lip, eye for Mask area . Similarly, we can also control the local makeup transfer degree of a single reference image by replacing the other reference input with the source image.

C.3 Preserving Skin Tone

Similar to previous approaches [17, 36, 47, 7, 46, 11, 46, 52], SHMT assumes that the foundations and other cosmetics have already covered the original skin tone. Therefore, the skin color of the reference face is considered as a part of its makeup styles and is faithfully transferred to the final generated result, which may corrupt the skin tone of the source image. To alleviate this problem, we can perform the above-mentioned local makeup

Reference1

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Lipstick

Eye Shadow

interpolation operation in the face region of the source image to preserve its skin tone. This procedure can be formulated as:

Here, the transferred result realizes the local makeup interpolation between the source image x and the reference image y 2 within the face area (excluding the lip and eye areas) in x , which is indicated by the mask Mask face . The interpolation results are visualized in Figure 14. When β = 0 , the transferred result will not change the skin tone of the source image. And when β = 1 , Equation (7) degenerates to the standard makeup transfer process in SHMT, which will distill the makeup information (including the skin tone) from the reference image to the source image.

C.4 More Comparison Results

Figure 15 and Figure 16 show more qualitative comparisons between SHMT and state-of-the-art methods on simple and complex makeup styles, respectively.

D Limitations

Our proposed model relies on the prior knowledge of the pre-trained models (face parsing and 3D reconstruction), and the stability of our model suffers when their output is inaccurate. As shown in Figure 17, the face parsing model often marks high-frequency makeup styles in the forehead area as hair and segments them into the background area, resulting in performance degradation.

E Social Impact

E.1 Social Impacts

Facial makeup customization offers an entertaining tool for generating realistic character photos. However, the long-term usage of makeup transfer techniques may increase users' appearance anxiety. And such technology may require access to personal biometric information, such as facial features, which could raise concerns about facial privacy if misused.

Preserve

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E.2 Safeguards

We will utilize the following strategies to mitigate negative impacts:

We will encrypt or anonymize facial images during transmission and storage, such as using hash values instead of real image data.

We will use the Stable diffusion safety checker 3 to conduct security checks on our generated images, so that we can identify and handle Not Safe For Work (NSFW) contents in images.

Since our method is working on human faces, we will also employ some deep-fake detection models [1, 6] to filter the results generated by our model.

We will ask the users to agree to a license or conform a code of ethics before accessing our model, which requires them to use our model in a more standardized manner.

E.3 Responsibility to Face Images

The face images in this study are taken from publicly accessible datasets, they're considered less sensitive. Furthermore, our data algorithm is strictly for academic purposes, not commercial use. During the inference stage, the proposed model adjusts only the makeup style without altering the individual's identity, thereby minimizing potential facial privacy concerns.

E.4 Data Availability

The MT [21], Wild-MT [17] and LADN [11] datasets that used in our experiments have already been released and can be found in the following links:

MT dataset: https://github.com/wtjiang98/BeautyGAN_pytorch .

Wild-MT dataset: https://github.com/wtjiang98/PSGAN .

LADN dataset: https://github.com/wangguanzhi/LADN .

PSGAN

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a diffusion-based makeup transfer method named Self-supervised Hierarchical Makeup Transfer (SHMT). The proposed method features a network that extracts makeup features from a distorted makeup image and aligns these features with facial features. The facial features consist of two components: one is the noisy generated feature at the current diffusion denoising timestep, and the other is derived from the shape of the source face. These two aligned facial features are then blended and incorporated into the denoising UNet as the makeup condition. Additionally, the face condition allows for fine-grained control of the transfer strength by using pyramid Laplacian features. The coarse Laplacian feature preserves more makeup details, while the fine Laplacian feature retains more details of the face being transferred.\n\nJustification: While using distorted images as a condition to fine-tune a diffusion model for conditioning on a distorted image's appearance is not particularly novel, this work introduces novelty through the control of makeup strength via Laplacian features. Additionally, the experimental section is comprehensive and impressive. For these reasons, I give rating Accept.\n\nSTRENGTHS:\n* The experiments are comprehensive, and the qualitative comparisons between different hyperparameters validate the model’s ability to handle both simple and complex makeup styles.\n* The model's ability to generalize to various types of makeup is impressive.\n\nWEAKNESSES:\n* It is unclear how the complexity of makeup is judged. Is it determined automatically or by human evaluation? If judged by humans, how is the parameter Laplace feature $h_i$ chosen during training?\n* typo: ln 109 Diffusio -> Diffusion\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nThe limitation has been discussed in the paper. \n\nThis study focuses on makeup transfer, necessitating experiments on human faces. Although this approach could potentially generate synthetic faces, the study's subject is makeup transfer rather than face manipulation. Since the human identity is not altered, the reviewer believes this should not raise forensic issues. (This is why I have chosen \"no ethics review is needed\" for next question.)", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-09"}

comments: SUMMARY:
This paper deals with the problem of makeup style transfer given a certain facial image. Current methods usually use synthesized ground truths to guide the model training, which is sub-optimal. This paper proposes to decompose the hierarchical texture details using a Laplacian pyramid and selectively introduce them to the content representation. Quantitative and qualitative analyses demonstrate the effectiveness of the proposed method.

STRENGTHS:
1. The problem of makeup style transfer to a given facial image is an interesting topic.
2. The paper is well written and I appreciate the figures in the paper.
3. The proposed SHMT method can flexibly control the preservation or discarding of hierarchical texture details, which achieves better  quantitative and qualitative results compared to current methods.

WEAKNESSES:
1. In Fig4, it seems the makeup transfer results could be influenced by the features of the reference image. For example, if the given reference image has a darker skin color, then the generated result would have a darker color, which might means the generation results could influenced by some irrelevant features other than the makeup style.
2. The comparison methods seem somehow out-dated to me, they could by replaced by some recent methods based on diffusion models.

QUESTIONS:
I have some questions about the chosen backbone, which seems a baseline model to me. Why didn't try some stronger diffusion models?

LIMITATIONS:
The authors discussed the limitations of their paper.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-10

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a method for improving the natural look resulting after makeup transfer. The method is derived from  Latent Diffusion Mode and is \"destroys\" the content to distillate the makeup, that is further difused. The method is evaluated on MT, Wild-MT, LADN datasets.\n\nSTRENGTHS:\n1. Visual appeal of the results: indeed the assumed purpose (i.e. more natural look) has been, in my view, achieved.\n2. The method is tuned for makeup as prior information about the face shape is incorporated.\n3. Objective evaluation  show method superiority. The evaluation is convincing.\n\nWEAKNESSES:\n1. Ethical concerns (please see below)\n2. The technical innovation is limited as mainly there are several prior works put together (although in a non-obvious way) and applied to a new theme (i.e. makeup transfer)\n3. The theme of the paper: \n - all the papers used in comparison have been published at ACM-MM, CVPR, ICCV. They are strong recent papers, there is nothing wrong with comparison. Yet,\n- NIPS is more machine learning oriented. Strictly from a machine learning point of view, the paper is not very interesting, as all the models are known. The particular novelty is by incorporating face shape\n - thus, the paper is more suitable to ACM-MM, CVPR, Face and Gesture, etc.\n\nQUESTIONS:\nBeyond the weaknesses, I do not have questions. I see the paper fair: it explains what has been done, it improves over prior art, the results look good.\n\nIn the rebuttal it would be nice to address, as much as possible, the ethical concerns. Some  emphasis could be on which \"checks\" might be useful.\n\nHowever, I still have concerns regarding the fit of the theme with NIPS. Unfortunately, the theme of the paper cannot be changed in the rebuttal.\n\nLIMITATIONS:\nUnfortunately, the ethical impact has not been addressed properly. The approach is Section 6 of the supplementary material:\n1. Potential risk: it has been accepted that \"Facial customization for makeup transfer offers an entertaining tool for generating realistic character photos. However, if misused, it could potentially produce false information.\" I agree with that : the risk has been acknowledged.\n\n2. Checks. It has been pointed that \"Moving forward, we should implement checks on generated photos to minimize any negative impacts.\" In only partially agree with that. The major difference in opinion is that, in my view, those \"checks\" should have been listed, or at least hinted and be part of the method (main paper). Now, having only a vague promise, it is too weak. \n \n3. Data license \n- It has been argued that \"The human images collected in this study come from publicly available sources with open copyrights. As most images feature public figures, they’re considered less sensitive.\" Further it has been pointed to \"Licenses for Datasets\" (supplementary - l 70). Yet at the respective URL locations there are licenses for software and not for images. In one case there is  accepted that no license is available.\n- it has been said that \"Furthermore, our data algorithm is strictly for academic purposes, not commercial use\". Yet in the main paper at l. 544 it has been said \"The release of the code is subject to the company’s permission, and we will do our best to release the code and trained models as soon as possible.\". The problem here is the contradiction between \"academic\" and \"our company\".\n\nIn summary:\nPro:\n  -  I believe that paper acknowledges the risk\nCon: \n - it does insufficiently to address them, delivering only a vague promise and to ensure that data (images) are properly used.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-11"}