SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models
Authors: Zhaoyang Sun
Shengwu Xiong
Yaxiong Chen
Fei Du
Weihua Chen
Fan Wang
Yi Rong
Venue: NeurIPS 2024 poster (2024)
Keywords: Makeup transfer
self-supervised learning
diffusion models
Primary area: diffusion_based_models
TL;DR: We propose a self-supervised hierarchical makeup transfer method that is flexible for both simple and complex makeup styles.

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
Zhaoyang S.n
Shengwu Xiong!
Yaxiong Chen'
Fei Du
Weihua Chen
Fan Wang
Yi Rot
School of Computer Science and Artificial Intelligence, Wuhan University of Technology
"Sayya Science and Education Innovation Park, Wuhan University of Technology
DAMO Academy, Alihaha Group
"Hupan Laboratory
'Shanghai AT Laboratory
Abstract
This paper studies the challenging task of makeup transfer, which aims apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. Addi- tionally, 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 novel Self-super Hierarchical Makeup Transfer (SHMT) method via latent diffusion models. Following "decoupling- cowuuplinn-andreeooooe aund-rcconstruction paradigm, SHMT works in a self-supervised manner, freeing itself from the misguidance imprecise pseudo-paie data. Furthermore, accommodate a variety of makeup styles, hierarchical texture details are decomposed via a Laplacian pyramid and selectively introduced the content representation. Finally, we design novel Iterative Dual Alignment (IDA) module that dynamically adjusts the injection condition of the diffusion model, allowing the alignment errors caused by the do- main 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: //gittttubb/ com/Snowfall ingplun/SSiii
1 Introduction
Recently, makeup transfer has become 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 have made significant advances in image realism and content preservation, the challenge of achieving transfer of various makeup styles still remains unsolved.
16
The difficulties of makeup transfer mainly stem from two aspects. On the one hand, makeup transfer essentially an unsupervised task, which means that there are real transferred images that be used as labeled targets for model training. To address this issue, previous methods typically synthesize "pseudo" ground truth from each input source-reference image pair, as an alternative supervision
"Work done during internship of Zhaoyang Sun DAMO Academy, Alibaba Group. t Cooresponding author: Yi Rong (yrong @ whul.edu.en).
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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(e) Sub-optinal Pseude-pcired Dala
Ambiguit Preserving Contents
Counetic
Prisere Discat Contest (Facki Eyslssoo)
Marhing
Figure 1: Illustration of two main difficulties in the makeup transfer task. (a) Due to the absence of paired data, previous methods utilize histogram matching or geometric distortion to synthesize sub-optimal pseudo- pseudo-paired data, which inevitably misguide the model training. (b) Some source content details should be preserved in simple makeup styles but be removed in complex ones.
Source details should be Preserved for stmple makeup sly
Resths
Source
Sourre content details should be Discanted for complex makeup sky les
Figure 2: addition to color matching, our approach allows flexible control to preserve or discard texture details for various makeup styles, without changing the facial shape.
signal. However, as shown in Figure [a), current pseudo-paired data synthesis techniques, including histogram matching (21 and geometric distortion [1] 41 36), fail to produce desirable outcomes. The reason that histogram-matte -ww--mmattthhhaiieee 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 coometrie-disoortio--be 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 [1 17 24
methods relies solely on the shape information (e.g.. facial
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 [b), the content details of a source face (e.g., freckles and eyelashes) usually should be preserved in 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-supervieed learning strategy following a "découpling--ndd-reca rendrrrconstruction paradigm. Specifically, given 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 self-supervised manner, thus climinating the misguidance of pseudo-paire data. To address the second issue,

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introduce a Laplacian pyramid 22 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 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 follows:
We propose a novel makeup transfer method, named SHMT, which employs a self-supervised learning strategy for model training, thus getting rid of the misleading weeudo-pairigg 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 state- of-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 [0] 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 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 spatially- invariant 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 [1 and SSAT [36 37) design 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 [ leverages multiple overlapping local makeup style discriminators focus on the -egg-frreeency makeup details. Additionally, CPM [29 applies segmentation model to predict the mask of the makeup pattern. This segmented pattern is then pasted into semantically
identical locations using UV g space.
Due to the unsupervised nature of makeup transfer, most of the above methods synthesize pseudo- paired data guide model training. In this strategy, the quality of these pseudo-paired data is critical, leading many works B
29 [4] to strive for better synthesis techniques. With the help of the unprecedented generative capabilities of both GPT-4V and Stable Diffusion, 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 self-supervised manner and eliminates the need for cumbersome pseudo-paire data synthesis.
41
36,
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 synthesis. DDPM
[5] proves the feasibility of recovering realistic images from random Gaussian noise. DDIM
(35]
enables fast and deterministic inference by transforming the sampling process into non- Mar-Mvako
3

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x(t 1)
U-Net
NoN
3
Mo
Zng
IDA
Xo
Bao Backggound
Projection
hi
Esd
Aligmment
2
Makeup
Em
ha
Ixs
Alignmennt
(1-w)
2m
ha
Content
U
E
Zm
Prediction
Hierarchical
h;
The details of IDA
texture details
Figure The framework of SHMT. facial image I is decomposed into background area log- makeup representation Im. and content representation (I3a- h2). The makeup transfer procedure is simulated by reconstructing the original image from these components. Hierarchica texture details h; are constructed to respond to different makeup styles. In each denoising step f, IDA draws on the noisy intermediate result f, to dynamically adjust the injection condition to correct alignment errors.
process. To reduce computational complexity while retaining high quality and flexibility, Latent Diffusion Models (LDM) (31 apply diffusion autoencoders. With training large datasets, both Imagen 33 and Stable Diffusio [1 elevate T21 synthesis to an unprecedented level.
model training in the latent space of powerful pretrained
Inspired by the above approach, we delve into the makeup transfer task based 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. process in the latent space to reduce the computational complexity of the model. In particular, LDM comprises three key components: image encoder E, decoder D, and UNet denoiser ca- Firstly, the encoder & compresses an image from the pixel space to a low-dimensional latent space 20 E(T). while the decoder D efforts reconstruct the original image from the latent variable zo (e.g. D(=n) = 2i Re I). Then, UNet denoiser eg is trained to predict the applied noise in the latent space. The optimization process can be defined the following formulation:
which performs the diffusion
Lidm =
co(z,, c, [|2]
(1)
where 2, is the noisy latent variable the timestep 4, e is the conditioned signal, e is randomly sampled from the standard Gaussian distribution, and T is the defined maximum timestep.
During inference, 27 is sampled from random Gaussian distribution. The UNet denoiser then iteratively predicts the noise in the latent space at each timestep and restores 2g via a sampling process (c.g. DDPM [1] or DDIM B.). Finally, 2p is reconstructed by the decoder D to obtain the generated image. See for more details.
3.2 Overview
The goal of makeup transfer is generate a new facial image that preserves the content information (e.g., background, facial structure, pose, expression) source image while applying the makeup style of reference image. To achieve this, propose SHMT, with its framework outlined in Figure

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3 In SHMT, we craft self-supervised strategy for model training in Section 3.3 and design an Itrattive Dual Alignment (IDA) module to correct alignment errors Section 3.4 The training and inference process of SHMT is presented in Section 3.5
3.3 Self-supervised Strategy
Following a
reconssrssttrtiino" 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 Tog (which includes hair and clothes) is known component. Hence, the background area Tog is input to the latent diffusion model along with the noisy image 7,- 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 ss content information. Specifically, apply sequence of spatial transformations to the foreground image fg-- These transformations include random cropping, rotation, and elastic distortion to create variation. As illustrated in Figure the distorted foreground image I.. 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
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 crucial component to preserve during makeup transfer. In this context, typical 3D face reconstruction model, known as D2 [13], is employed to extract the face shape Isd from the original image To match the resolution of the latent space of LDM, we perform a pixel unshuffle operation 34] to downsample Iad-
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 Ifg is first converted to a grayscale image Irg of h x w pixels. Then we downsample it by applying a fixed component ho- which is treated texture details, can be calculated as ho i - 11, where I is upsampled from 41- By replacing f with 1 and repeating these operations, we obtain series of hierarchical texture details [ho, , 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.
Texture Details:
Gaussian kernel to produce low-pass prediction 1 E IR 3 _ The
Note that only one texture detail h; is concatenated to the face shape J3d as a complete content representation. When h; is 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; is a coarse texture detail, our model must also distill the high- frequency 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:
fa(i) S ( M(ij)) (lfa(() fm
(2)
5

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where fc = Ec(Isd. h4), fm = (E(m) denote the semantic features extracted by encoders E. and Em. f() represents the feature vector of the i-th pixel and (i.j) indicates the element at the (ith) location of M. We consider the correlation matrix M as a deformation mapping function, and use it to spatially deform the feature maps 2m = (TT,) m) of makeup representations:
2m Σ So Softmar ((, Zm.
where Softmaz(-) denotes a softmax computation along the column dimension, which normalizes the element values in each of M, and > is temperature parameter. Theoretically, the deformed feature maps 2m 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 2m. Considering the property that noisy intermediate result L is gradually moving closer the real image domain (c.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 zm between the noisy intermediate result I, and makeup representation Im to dynamically correct the previous alignment prediction zmm- I, is the result of 1 removing the background area. Since the noise degree of is determined by timestep t, MLP module predicts the percentage w of two alignment predictions from timestep ::
2m w) + W.Z.m
44)
where MLP(() and the MLP module consists of two fully connected layers and ends with a Sigmoid activation layer. Finally, the mixed prediction ém is concatenated with the content representation and injected into the denoiser's encoder through a projection module consisting of 1 x 1 convolution. In addition to correct alignment errors, IDA has two other advantages. First, because &m is semantically aligned with the content representation, IDA can control the makeup style a spatially-aware manner. Corresponding results are displayed the supplementary material. Second, IDA is relatively lightweight, with only approximately IMM parameters.
3.5 Training and Inference
During training, the parameters of the pre-trained autoencoder are fixed, the U-net denoiser ég and IDA module are jointly optimized from scratch under the original objective function in Equation [ At inference, the model receives the background area bo and content representation (Jaa and h2) from the source image, well as the undeformed makeup representation Im from the reference image, to generate the makeup transfer result.
Experiments
4.1 Experimental settings
Datasets. Following [21 we randomly select 90% of the images from the MT dataset 21 as training samples and the rest 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 number of images with complex makeup styles.
Implementation Details. In our experiments, we discover that the autoencoder with downsampling factor of 4 preserves texture details better than the one with factor of 8. Therefore, the autoencoder with downsampling factor of 4 is selected, and the SHMT model is trained at a resolution of 256 x 256. The specific structure of the UNet denoiser Eg remains the same as the LDM [3I], with IDA module replacing the original conditional injection module. In Equation 3 is set to 100. We train the model with Adam optimizer, learning rate of le-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 metries, FID, CLS and Key-sim. Following (24), FID [4 is calculated between reference images and transferred results to indicate image realism. The lower the FID, the

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Source
Reference
PSGAN
SCGAN
EleGANt
SSAT
LADN
CPM
SHMT-ho
Figure 4: Qualitative comparison with GAN-based baselines on simple makeup styles.
Source
Reference
PSGAN
SCGAN
EleGANt
SSAT
LADN
CPM
SHMT-h4
Figure Qualitative comparison with GAN-based baselines on complex makeup styles.
better. Recently, the work 33 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 image, respectively. Inspired by this, we compute 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 reflect the content preservation. The higher the CLS and Key-sim, the better.
Baselines. We choose seven state-of-the-art makeup transfer methods baselines, including PSAGN [1]. SCGAN 77 EleGANt 47), SSAT [36). LADN [I. CPM[29] and Stable-Makeup 52 Among them, only StableMakkur is diffusion-mont mode--ooleeleleeeeee based method, while the others are GAN based methods. And LADN, CPM and Stable-Makeuy Makeup focus on complex makeup styles. In our experiments, baseline results are derived from official publicly available code 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 and Figure respectively. Although PSGAN, SCGAN, EleGANt, and SSAT preserve the content the source image well, they have low fidelity for reference makeup, especially in complex makeup styles. In addition, they have tendency to modify
7

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Source
Reference
Stable Makeup
SHMT-RO
Figure 6: Qualitative comparison with the Stable-Makeup baseline simple makeup styles.
Source
Reference
Stable Makeup
SHMMT-h4
Figure 7: Qualitative comparison with the Stable-Makeup baseline on complex makeup styles.
the background color, e.g., the second and fourth rows of Figure LADN's results are accompanied by 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 noticeable sense of pasting, and there are some artifacts along the facial contour. The results of cnffusiion-moeel-iee
methods are shown in Figure 6 and Figure 7 For simple makeup styles, the results of Stable-Mai Makeup show noticeable color shift when compared to the reference makeup styles. 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 significant loss of high- high-frequency makeup details. In contrast, SHMT method can naturally and accurately reproduce various makeup styles the source face by equipping it with different texture details.
Quantitative Results. In experiments, randomly selected 1000 source-reference image pairs from the test set each dataset to calculate each evaluation metric. The quantitative results of the different methods are listed in Table [ SHMT-hg equipped with fine texture details fy obtains the highest value on Key-sim, indicating better content preservation. SHMT-ha equipped with coarse texture details h4 achieves the highest values FID, CLS, suggesting greater image realism and makeup fidelity. This also demonstrates that there a tradeoff between content preservation and makeup fidelity in the makeup transfer task.
User Study. We also perform 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

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Table I: Quantitative results of FID, CLS and Key-sim the MT. Wild-MT and LADN datasets.
MT
Wild-MT
LADN
Methods
FID
CLS
Key-sim
FID
CLS
Key-sim
FID
CLS
Key-sim
PSGAN 17
45.02
0.628
0.975
89.92
0.642
0.969
57.80
0.684
0.975
SCGAN 7
39.20
0,636
0.965
79.54
0.660
0.976
51.39
0.685
0.973
EleGAN: 47
54.06
0.634
0.973
86.19
0.651
0.961
61.40
0.693
0.969
SSAT 36
38.01
0.645
0.975
70.53
0,667
0.973
53.84
0,692
0.976
LADN |
73.91
0.620
0.917
104.91
0.634
0.914
65.87
0.688
0.930
CPM 29
42.76
0.652
0.951
95.61
0.661
0.924
40.57
0.729
0.954
Stable-Makeup
52
33.26
0.682
0,973
64.64
0.711
0.968
37.33
0.767
0.965
SHMT-Ao
32.24
0.658
0.976
51.54
0.668
0.976
38.97
0.711
0.978
SHMT-h4
24.93
0.715
0.953
45.02
0.719
0.954
27.01
0.786
0.958
Table 2: The ratio selected as best (%)
Table 3: Quantitative results of IDA on LADN dataset.
Styles
EleGANt
SSAT
CPM
SHMT
Methods
CLS
Key-xim
Simple 20.25% 1.75%
13.5%
4.75% 11.5%
61.5%
SHMT-h4 w/o IDA
32.42
0.753
0,960
Complex
0%
86.75%
SHMT-/14
27.01
0.786
0,958
results using different methods. Then, participants are asked to choose the most satisfactory result, considering image realism, content preservation, and makeup fidelity. To ensure fair comparison, the transferred results are displayed simultaneously in random order. The results the user study shown in Table 2
4.3
Ablation Study
The effectiveness of hierarchical texture details. Figure (a) illustrates the visual comparison of SHMT with varying texture details handling both simple and complex makeup styles. SHMT-ho tends to preserve high-frequency details of the source image, such as subtle expressions, single and double eyelids, eyelashes, and freckles. is more suitable for simple makeup transfer. On the other hand, SHMT-ha 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 different model to predict the noise, i.e., using SHMT-ho for [T,I] and SHMT-h4 for (t,0), we can easily achieve a seamless interpolation between two results.
The effectiveness of IDA. To verify the effectiveness of IDA, we remove the additional alignment prediction =m and replace å.. with En as an injection condition for the denoiser. We retrain the SHMT-ha model, and the alignment visualizations and transfrred results are shown Figure 3 (b). As seen, the proposed IDA module effectively corrects alignment errors and enhances makeup fidelity. The quantitative results are displayed in Table In Figure (c), the trend percentage w over time is investigated. As timestep becomes smaller, the domain gap between the noisy intermediate result i, and makeup representation Im gets smaller, and increases indicating that the model favors more the alignment prediction of these two. In addition, w of SHMT-/4 significantly higher than that of SHMT-/1 at each timestep f, 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 g (a), calculating the semantic alignment between the source and reference images makes our model insensitive 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 (b).

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(a) The effectiveness of hierarchical texture details
Simple
Complex
Source
Reference
SHLME-hy
Seamless Interpolation
SHMT in
(b) The effectiveness of IDA
(c) The trend w
255
Wie IDA Figure 8: Ablation studies of each proposed module to validate its effectiveness. Zoomed-in view for a better comparison.
Source
Reference
wie IDA Alignment
IDA Alignment
W/ IDA
Smosllp
(8) robustness of model
(b) The generalization model
Meferrance
Seurce
Searce
Reforense
Rosates
Elderdy
Figure The robustness and generalization ability of the model SHMT-hg in various scenarios.
4.4 Limitations
The limitations of our method be summarized in two aspects. First, our proposed model relies the prior knowledge of the pre-trained models (face parsing and 3D) reconstruction), and the stability of our model suffers when their output inaccurate. More results and discussion 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.
Conclusion
In this paper, we propose Self-supervised Hierarchical Makeup Transfer (SHMT) method. It employs supervieed strategy for model training, freeing itself from the misguidance of pseudo- paired 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.
10

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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. SCKJ- JYRC-2022-76. SU-C-2022-PTDXX-
the Project of Sanya Science and Education Innovation Park of Wuhan University of Technology (Grant No. 202 KF003 1), Alibaba Group through Alibaba Research Intern Program.
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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, model SHMT has to leam high-frequency details from the makeup representation, thus adapting verify this, we train SHMT model equipped with different texture details separately, and corresponding results are shown in Figure [0] From dop to /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 As the metric CLS gradually increases, the metric Key-sim gradually decreases, which also indicates tradc-off between makeup fidelity and content preservation.
complex makeup styles. To
PUID
Source
Reference
SHMT-hg
SHMT-h
SHMT-h2
SHMT-h3
SHMT-h4
Qualitative results of models equipped with different texture details under complex makeup styles. As the texture goes from fine to coarse, the model gradually tends to transfer high-frequency texture details from the reference images.
Figure 10:
Table 4: The quantitative results of models equipped with different texture details the LADN dataset.
Methods
FID
CLS
Key-sim
SHMT-ho ho
38.97
0.711
0.978
SHMT-/ /
38.86
0.712
0.978
SHMT-h2
36.35
0.726
0.973
SHMT-
33.12
0.748
0.965
SHMT-h4
27.01
0.786
0.958
B Comparison with InstantStyle
We also compare the proposed method with the recent style transfer method InstantStyle [42]. The qualitative results shown in Figure [T In InstantStyle [42).
the prompt woman, best quality, high quality", the number samples set 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.
15

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InstantStyle
Source
Reference
SHMT
Prompt: a woman, best quality, high quality
Figure 11: Qualitative comparison of our method SHMT with the style transfer method InstantStyle.
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 2m. This allows to interpolate the makeup styles between two different reference faces by linearly fusing their the feature maps &m, as follows:
(S) Here êm1 and êmz are alignment predictions of two different reference images. respectively. By adjusting the value of from to 1, SHMT generate series of transferred results. Their makeup styles will gradually change from that of one reference image Y1 that of the other y2. Moreover, by assigning source image as y,, we can control the degree of makeup transfer for single reference input ya. The global makcup interpolation results are shown in Figure 12]
im (1 2m 38m2-
C.2 Local Makeup Interpolation
In SHMT, the alignment prediction Sm 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 follows:
2m ((1 - 8m + (2m2) @ M Maskiarea + 2m_self & (1 Maskerea),
(6)
where @ denotes the Hadamard product and _m__etf self denotes the alignment prediction by assigning the source image reference image. Ad askarea
binary mask be makeup, which can obtained by face parsing. Figure ( the local makeup interpolation results within the areas around the lips and eyes, respectively, i.c., area E lip, eye for Maskarea- Similarly, we can also control the local makeup transfer degree of single reference image by replacing the other reference input with the source image.
source image z, indicating the local areas to
C.3 Preserving Skin Tone
52), SHMT assumes that the foundations other cosmetics have already covered the original skin tone. Therefore, the skin color of the reference face considered 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, can perform the above-mentions local makeup
Similar to previous approaches 17
16

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Source
Reference1
Global Seamless Interpolation
Reference2
Figure 12:
The illustration of global makeup interpolation. The first row is the result of a single reference image, the second row is the result of two reference images...
Lipatick
Shadow
Source
Reference1
Seamless Interpolation
Reference2
Figure 13: The illustration of local makeup interpolation. The first row is lipstick control, the second row is eye shadow control.
interpolation operation in the face region the source image to preserve its tone. This procedure can be formulated as:
2m ((1 - ((11-))am_ce self + (2m2) & Maskface + êm_self &0 (1 Maskface).
Here, the transferred result realizes the local makeup interpolation between the source image x and the reference image y/ within
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 / = 0, the transferred result will not change the skin tone of the source image. And when 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 and Figure shoshow qualitative comparisons between SHMT and state-of-the-ar methods 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 inaccurate. As shown in Figure [7] the face parsing model often marks high-frequency makeup styles in the forehead 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 generating realistic character photos. However, the long-ter log-ttrrman usage of makeup transfer techniques may increase users' appearance anxiety. And such technology may require access to personal biometric information, such faciall features, which could raise concerns about facial privacy if misused.
17

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Preserve
Change Skin Tone
Seamless Interpolation
Skin Tone
Figure 14: By default, our method transfers makeup to change the skin tone. Optionally, the local makeup transfer operation can preserve the original skin tone, and the local makeup interpolation can smoothly generate intermediate results.
E.2 Safeguards
We will utilize the following strategies to mitigate negative impacts:
I) We will encrypt or anonymize facial images during transmission and storage, such as using hash values instead of real image data.
2) We will use the Stable diffusion safety checker to conduct security checks generated images, so that identify and handle Not Safe For Work (NSFW) contents in images.
Since method working human faces, will also employ some deep-fake detection models || 6 filter results generated by our model.
the users to agree to license or conform code 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 taken from publicly accessible datasets, they're considered less sensitive. Furthermore, our data algorithm strictly for academic purposes, not commercial use. During the inference stage, the proposed model adjusts only the makeup style without altering the individual's 's identity, thereby minimizing potential facial privacy concerns.
E.4
Data Availability
The MT 21]. Wild-MT 17 and LADN III datasets that used in our experiments have already been released and can be found in the following links:
MT dataset: http:p////aataaoo////aaa/agaaa/
pytorch
Wild-MT dataset http://///ggiiiib.oo//aaan//////
LADN dataset: https://github. http://hhbb..cmmaagggauaa///////
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Source
Reference
PSGAN
SCGAN
EleGANt
SSAT
LADN
CPM
SHMT-ho
Figure 15: More qualitative results of different methods in simple makeup styles.
19

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Source
Reference
PSGAN
SCGAN
EleGANt
SSAT
LADN
CPM
SHMT-
Figure 16: More qualitative results of different methods in complex makeup styles.
20

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Source
Reference
Face parsing
SHMT-h4
Figure 17: Limitations of our approach. The face parsing model often marks high- high-frequency makeup styles in the forehead area as hair and segments them into the background area, resulting in performance degradation.
21

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27


Reviews:
[{"review_id": "Jk19kdcqTK", "reviewer": "Reviewer_JmHE", "summary": "The 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.", "questions": "Beyond 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.", "limitations": "Unfortunately, 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.", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. 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.", "weaknesses": "1. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> 1. 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). \n2. Strictly from a machine learning point of view, the paper is not very interesting, as all the models are known.\n\nThank you for your question. But we cannot agree with your comments saying that “The technical innovation is limited as mainly there are several prior works put together.” Specifically, our method develops a new self-supervised learning strategy for training the diffusion-model-based makeup transfer framework without generating pseudo ground truths as previous works. **This strategy not only makes the semantic alignment between the makeup image $I_m$ and the intermediate noisy image $\\hat{I_t}$, but also semantically align $I_m$ with the content details $I_{3d}$ and $h_{i}$.** In this way, the makeup styles in reference images and the content information in source images can be fully integrated into each diffusion denoising step, so that the generated performance can be improved. Moreover, we also associate different frequency components (decomposed by a Laplacian pyramid) with different complexity levels of makeup styles, and **for the first time unify the modeling of both simple and complex makeup style information in a single framework, which is a novel technique that has not been explored in previous works.** This novelty is also approved by Reviewer dGT5 “this work introduces novelty through the control of makeup strength via Laplacian features.” Based on the above analysis, we believe that our proposed method exhibits sufficient technical innovation.\n\nWe also cannot agree with the comments that “the paper is not very interesting, as all the models are known.” In our opinion, it would be unreasonable to give our paper a negative rating for this reason. Although the basic network components of our framework have been proposed in previous works, we utilize them to **solve a novel academic problem, i.e., how to design a unified framework that can preserve the source content details for simple makeup styles and also discard those details for complex makeup transfer.** Such motivation has been clearly stated in our introduction section. To solve this problem, we explore to employ the diffusion model to establish our SHMT framework (existing methods are built based on GAN), and integrate the novel designs mentioned in the above paragraph. Therefore, we believe that our paper has proposed a novel method to solve a new problem, which will make a specific contribution to relevant research community.\n\n> 3. The theme of the paper: 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, 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, thus, the paper is more suitable to ACM-MM, CVPR, Face and Gesture, etc. \n\nThank you for your comments. We quote the call for paper of NeurIPS 2024 from its official website [https://neurips.cc/Conferences/2024/CallForPapers] as follow:\n\n> “The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, **computer vision**, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following:\n>\n>•\t**Applications (e.g., vision, language, speech and audio, Creative AI)**\n>\n>•\tDeep learning (e.g., architectures, **generative models**, optimization for deep networks, foundation models, LLMs) ”\n\nThe makeup transfer task and our paper clearly align with the topics of **Applications (vision)** and **Deep learning (generative models)**. Moreover, our novelty is not only “incorporating face shape”. As mentioned in the first question, we deign a new self-supervised training strategy based on double semantic alignment to avoid the negative effects of the pseudo ground truths. In addition, we also integrate the modeling of both simple and complex makeup into a unified framework, which enables the users to control the makeup strength more flexible. Finally, extensive quantitative and qualitative analyses demonstrate the effectiveness of our method. Therefore, we think that our paper is fit the theme with NeurIPS 2024.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "stodfSyZWR", "reviewer": "Reviewer_izVu", "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.", "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.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The problem of makeup style transfer to a given facial image is an interesting topic.\n2. The paper is well written and I appreciate the figures in the paper.\n3. 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.\n2. The comparison methods seem somehow out-dated to me, they could by replaced by some recent methods based on diffusion models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> 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.\n\nThank you for your question. In real life, the heavy use of cosmetics can cover up an individual's original skin tone, especially with some complex makeup styles. To the best of our knowledge, nearly all existing makeup transfer methods, including our approach, are unable to distinguish whether the skin color in the reference image is natural or cosmetically altered. Therefore, these methods typically assume that the makeup process will change the original skin tone and do not consider the skin tone preservation. To address this issue, we provide a solution in Section 3.3 Preserving Skin Tone of the supplementary materials. Specifically, we can utilize local makeup interpolation operations to flexibly control the extent of skin tone preservation, as demonstrated in Figure 5 of the supplementary materials.\n\n > 2. The comparison methods seem somehow out-dated to me, they could by replaced by some recent methods based on diffusion models. \n\nThank you for pointing out this issue. To the best of our knowledge, there is only one makeup transfer method based on the diffusion model, namely StableMakeup [1], which is currently preprinted on arXiv. We also cite this reference in our related work section as **''With the help of the unprecedented generative capabilities of both GPT-4V and Stable Diffusion, a concurrent work produces higher quality pseudo-paired data, thereby improving the performance of makeup transfer.''** This method still needs to construct pseudo ground truths (PGTs) for model training, while our proposed method adopts a fully self-supervised training strategy, allowing it to avoid the negative effects of PGTs. Nevertheless, during the peer review process of NeurIPS, the authors of StableMakeup release their source code, so we make additional quantitative comparisons (please see Table 1, Figure 1 and Figure 2 in the Global Author Rebuttal PDF). Moreover, we also compare our method with InstantStyle [2], a diffusion model for style transfer tasks. From both the qualitative and quantitative results, we can find that SHMT consistently outperforms these two state-of-the-art diffusion-model-based approaches, indicating the effectiveness of our method. We will also add these results to the final version of our paper.\n\n[1] Zhang, Yuxuan, et al. Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model. arXiv preprint arXiv:2403.07764, 2024.\n\n[2] Wang, Haofan, et al. Instantstyle: Free lunch towards style-preserving in text-to-image generation. arXiv preprint arXiv:2404.02733, 2024.\n\n> 3. I have some questions about the chosen backbone, which seems a baseline model to me. Why didn't try some stronger diffusion models?\n\nThank you for your question. In this paper, we choose the original LDM as our backbone based on following reasons: 1) We need to train our model from scratch, but our computational resources are limited. Considering training costs and time used, we select to use the LDM model. 2) The major contributions of this paper are to design a fully self-supervised diffusion-model-based makeup transfer framework and incorporate the Laplacian pyramid to hierarchically characterize the texture information. Pursuing better performance **by replacing more powerful backbones** is not our main purpose. And moreover, the experiments in our paper demonstrate that using LDM as the backbone can already lead to superior results than previous works. Nevertheless, we believe that employing a stronger diffusion backbone like SD v1.5 or SDXL v1.0 can further empower our SHMT framework and we leave exploring these models in our future works. We appreciate your valuable suggestion.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yho93fWYqA", "reviewer": "Reviewer_dGT5", "summary": "This 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.", "questions": "See weaknesses", "limitations": "The 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.)", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "* 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.", "weaknesses": "* 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", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> 1. 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 chosen during training?\n\nThank you for your question. In our opinion, we believe that each customer who uses a makeup transfer application has his or her own understanding and judgement about the complexity of the target makeup styles in the reference images. Establishing a unified standard for such judgement cannot adaptively meet the needs of all customers. Therefore, our proposed method provides the users with the flexibility to make their own decisions about what makeup information should to be transferred. More specifically, during the training phase, we do not choose Laplace features for each single input image. Instead, we fix a specific texture detail and train the model to transfer the decomposed information at the corresponding frequency level. As a result, we simply train five different models (SHMT-$h_i$, $i=0,1,2,3,4$) by consistently incorporating $h_i$ texture details of all the images, as presented in the Section 1 **The effectiveness of hierarchical texture details** of the supplementary materials. After that, during the inference stage, the users can select and combine different models to generate transferred results depending on their own requirements.\n\n> 2. typo: ln 109 Diffusio -> Diffusion.\n\nThank you for pointing out this issue. As suggested, we have corrected this typo and also carefully checked the whole paper to avoid such mistakes.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Here, we attempt to solve the ethical concerns of Reviewer JmHE as follows:
> 1. Ethical concerns: 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. 

Thank you for your question. Our proposed SHMT model mainly focuses on transferring the makeup styles from the reference image to the source face, which are two user-specified inputs. During the transfer procedure, the identity information of both source and reference faces will be maximumly preserved. This is also supported by reviewer dGT5 that “This 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.” And moreover, our SHMT trains the LDM from scratch rather than fine-tuning a pre-trained model, which will prevent the obtained model from memorizing the characteristics of particular individuals and also avoid to generate a person-specific image without providing source image. This further circumvents potential ethical risks.

> 2. Ethical concerns: 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.

Thank you for pointing out this issue. We list the specific checking operations as follows:

1. We will utilize the Stable diffusion safety checker [https://huggingface.co/CompVis/stable-diffusion-safety-checker] to conduct security checks on our generated images, so that we can identify and handle Not Safe For Work (NSFW) contents in images.

2. Since our method is working on human faces, we will also employ some deep-fake detection models [1][2] to filter the results generated by our model.

3. 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 more standardizedly. 

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

[2] Corvi, Riccardo, et al. On the detection of synthetic images generated by diffusion models. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.

We will clearly list the above checking operations in the final version of our paper, and we hope these measures can alleviate your concerns about the checking procedure.

> 3. Ethical concerns: Data license: 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. 

Thank you for pointing out this issue. We have checked the URL in our paper, and indeed only found the licenses for the software. We will correct these errors in the final version of our paper. Nevertheless, the datasets used in our paper is downloaded from the link provided by their owners (we have properly cited the related original papers and provided the URL locations of the corresponding projects). And moreover, the MT, Wild-MT and LADN datasets are three popular publicly available datasets that have been widely used in existing makeup transfer or privacy protection approaches [1][2][3]. Therefore, we believe that the usage of these datasets has not violated the license of existing assets for NeurIPS 2024.

[1] Yang, Chenyu, et al. Elegant: Exquisite and locally editable gan for makeup transfer. European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022.

[2] Shamshad, Fahad, Muzammal Naseer, and Karthik Nandakumar. Clip2protect: Protecting facial privacy using text-guided makeup via adversarial latent search. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.

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

> 4. Ethical concerns: 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".

Thank you for pointing out this. This paper is an outcome of a collaborative project between our university and a company, which is a fully academic research project rather than a business one. Therefore, the algorithm designed in our paper is strictly for academic purposes, not for commercial use. However, the release of the source code requires the agreement of both parties, so we said that “The release of the code is subject to the company’s permission”. We promise that we will release the code and the model once this paper is accepted.

Meta-review: The technical part and presentation of this paper have been accepted by three reviewers. The paper is well written and presented with interesting and appealing figures. The paper proposes a self-supervised learning method for facial makeup transfer. The proposed method is reasonable and effective, as agreed by reviewers. Authors committed to open-source their code, including training code. The only concerns that have not been well agreed between reviewers and authors are three folds:

(1) Novelty. Reviewers considered that this paper did not bring big innovations to the field, as most of the components are known techniques. However, reviewers agreed that combining the different components to address the makeup transfer problem is not trivial, and particularly the new way of preserving face shape and using the Laplacian pyramid for fine-grained texture transfer. Authors proved the effectiveness of the proposed framework, and showed different configuring ways of the proposed method, as well as the ablation of different components. Overall, AC thinks that the new design is novel enough and especially effective for the focused problem.

(2) Fitness to NeurIPS. One reviewer raised that the paper is not suitable for publication in NeurIPS, mainly due to the lack of machine learning content. After reading the paper, AC regards that though no theoretical machine learning method and proof are presented in this paper, the proposed self-supervised learning method in addressing makeup transfer is still interesting, and presented with clear equations. It is effective in addressing the issue of no paired real-world ground-truth data is available for training. Besides, computer vision in NeurIPS also accepts applied techniques with good performance and deep insights. It's good to see that the paper presents many good insights of the proposed method, including how to control the model behaviours, and what the advantages and limitations are. All the aboves would benefit the computer vision community and perhaps the machine learning community as well in addressing how to design an effective self-supervised learning method for the makeup transfer task where there is no groundtruth, and potentially beyond this tailored task in inspiring other similar learning scenarios.

(3) Ethics issues. The major disagreement among reviewers is the ethics review. Two reviewers thought that no ethics concern existed, while the other reviewer thought the ethics concern was serious in this paper. Additionally, two ethics reviewers helped in providing their expert views of ethics concerns in this paper. They thought that the ethics concerns can be addressed, and provided with useful suggestions in dealing with data usage and the negative impacts regarding face generation. Authors responded positively; they confirmed some actions that had been taken, and some future actions that would be implemented. AC thinks that these positive steps are useful and should be enough to address the ethics concerns. 

Though many previously accepted papers did not perform such steps in protecting privacy and prevent negative social impacts and so it appears that it is a bit too strict for the regulation of this paper (from authors' point of view), AC agrees that to make our community healthy and push forward proper use of AI techniques, we should step by step require authors to pay more attention to these ethics concerns. I think authors of this paper have already understood this and agreed to implement the suggested actions. However, this AC would also suggest reviewers and our organizing committees do not over-emphasize or over-interpret the ethics guidelines; otherwise, it would also be a potential harm to the research community in conducting their research and get papers accepted. For example, for dataset usage, we may need to enforce the regulation step by step but not in a hurry. For historical reasons, many existing datasets have not been developed in an ethical way but there have already been many results published on those datasets while replacements may not be available. In this regard, authors have to compare their results to published results. As long as those popular datasets have not been officially withdrawn or be banned by the organizing committee, this AC thinks that authors are still permitted to use those popular datasets at the moment, but certainly, in a guide of knowing the risks and positively looking for replacements. A counter example is the Duke-MTMC dataset, which has already been officially retracted and both NeurIPS and CVPR conferences explicitly require removing this dataset from the submitted papers or otherwise they cannot be accepted; besides, there are many replacements to this dataset.

To conclude, AC regards that this paper presents good value to the community and thus is happy to accept it for publication. Authors are required to include necessary discussions in the rebuttal to the camera-ready version of this paper, implement suggested actions to address the ethics concerns, and the most important, release the source code including the training code to the public as committed, so as to allow other researchers to reimplement the proposed method, verify the results, and implement further improvements.
Decision: Accept (poster)