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_cc_2024_Conference
Published: 2024-05-06 05:51:21

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.

Main paper: 
 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 * Work done during internship of Zhaoyang Sun at DAMO Academy, Alibaba Group.† Corresponding author: Yi Rong (yrong@whut.edu.cn).38th Conference on Neural Information Processing Systems (NeurIPS 2024). 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:• 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 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 WorksQuestion: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?Answer: [Yes]Justification: In the abstract and introduction, we provide a detailed comparison of the differences with existing approaches, accurately reflecting the contribution and scope of the paper. Please refer to Abstract and Section 1 for details.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 
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.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 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 MethodologyQuestion: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: In the Limitations section, we discuss the limitations of our approach and provide explicit examples in the supplementary material. Please refer to Section 4.4 and supplementary material for details.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
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:EQUATIONwhere 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.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.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 f g . 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 3d 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 3d .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 f g is first converted to a grayscale image Îfg of h × w pixels. Then we downsample it by applying a fixed Gaussian kernel to produce a low-pass predictionl 1 ∈ R h 2 × w 2 .The high-frequency component h 0 , which is treated as texture details, can be calculated as h 0 = Îfg -l1 , where l1 is upsampled from l 1 . By replacing Îfg 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 3d 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.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:EQUATIONwheref c = E c (I 3d , 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:EQUATIONwhere Sof tmax(•) 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 EQUATIONwhere w = MLP(t) and the MLP module consists of two fully connected layers and ends with a Sigmoid activation layer. Finally, the mixed prediction ẑm 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 ẑm 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.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 3d 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.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [NA] Justification: This paper does not include theoretical results.Guidelines:• The answer NA means that the paper does not include theoretical results.• All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.• All assumptions should be clearly stated or referenced in the statement of any theorems.• The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. • Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material. • Theorems and Lemmas that the proof relies upon should be properly referenced. 
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 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.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 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 . SHMT-h 0 equipped with fine texture details h 0 obtains the highest value on Key-sim, indicating better content preservation. SHMT-h 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 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. SHMT-h 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, SHMT-h 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 SHMT-h 0 for [T,t] and SHMT-h 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 ẑm with z ′ m as an injection condition for the denoiser. We retrain the SHMT-h 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 Ît 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 SHMT-h 4 is significantly higher than that of SHMT-h 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) . 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.Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?Answer: [Yes] Justification: Our paper fully discloses all the information needed to reproduce the main experimental results of the paper. 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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.Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] Justification: 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.Guidelines:• The answer NA means that paper does not include experiments requiring code. Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [No] Justification: Following the previous approaches, we randomly selects 1000 pairs of test samples and reports only the average metrics. 
experiments compute resources: 
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 Appendix: 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 h0 to h4, 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. 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.InstantStyle Prompt: a woman, best quality, high quality SHMT In our proposed method, the makeup information is decoupled from the input images and encoded into the feature maps ẑm. This allows us to interpolate the makeup styles between two different reference faces by linearly fusing their the feature maps ẑm, as follows:EQUATION)Here ẑm1 and ẑm2 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 y1 to that of the other y2. Moreover, by assigning the source image as y1, we can control the degree of makeup transfer for a single reference input y2. The global makeup interpolation results are shown in Figure 12 .In SHMT, the alignment prediction ẑm 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:EQUATIONwhere ⊗ denotes the Hadamard product and ẑm_self denotes the alignment prediction by assigning the source image as a reference image. M askarea 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 M askarea. 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.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 interpolation operation in the face region of the source image to preserve its skin tone. This procedure can be formulated as:EQUATIONHere, the transferred result realizes the local makeup interpolation between the source image x and the reference image y2 within the face area (excluding the lip and eye areas) in x, which is indicated by the mask M ask f ace .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.Figure 15 and Figure 16 show more qualitative comparisons between SHMT and state-of-the-art methods on simple and complex makeup styles, respectively.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.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.Seamless Interpolation Change Skin Tone We will utilize the following strategies to mitigate negative impacts:1) 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 3 to conduct security checks on our generated images, so that we can identify and handle Not Safe For Work (NSFW) contents in images.3) Since our method is on human faces, we will also employ some deep-fake detection models [1, 6] to filter the results generated by our model. 4) 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.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.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. Face parsing SHMT-! " NeurIPS Paper Checklist• The answer NA means that the paper does not include experiments. • The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions). • The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors). • It should be clear whether the error bar is the standard deviation or the standard error of the mean. • It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified. • For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates). • If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.https://huggingface.co/CompVis/stable-diffusion-safety-checker 


Decision: 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.

Reviews: review: paper_summary: paper_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.
strength_weakness: strength_weakness: 1. Ethical concerns (please see below)
2. 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)
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.
questions: 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.

In 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.

However, 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: limitations: Unfortunately, the ethical impact has not been addressed properly. The approach is Section 6 of the supplementary material:
1. 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.

2. 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. 
 
3. 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.
- 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".

In summary:
Pro:
  -  I believe that paper acknowledges the risk
Con: 
 - it does insufficiently to address them, delivering only a vague promise and to ensure that data (images) are properly used.
paper_summary: paper_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.
strength_weakness: strength_weakness: 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: 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: limitations: The authors discussed the limitations of their paper.
paper_summary: paper_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.

Justification: 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.
strength_weakness: strength_weakness: * 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?
* typo: ln 109 Diffusio -> Diffusion
questions: questions: See weaknesses
limitations: limitations: The limitation has been discussed in the paper. 

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. (This is why I have chosen "no ethics review is needed" for next question.)

Comments: title: The response of the ethical concerns
The response of the ethical concern
Thoughts on ethical concerns
Contribution to machine learning
Open-source the code of our SHMT method
The response of the ethical concerns
comment: > what are broader implications of using this domain (faces) and what are ways to mitigate it (there must be literature on this)

Thank you for your question. We list some possible broader implications and provide ways to mitigate it.
#### Broader Implications:
Facial Privacy. Makeup transfer technology may require access to personal biometric information, such as facial features, which may raise concerns about facial privacy. 

Social Impact. Long-term use of makeup transfer technology may increase users' appearance anxiety.
#### Mitigation Strategy:

Anonymization Processing. Encrypt or anonymize facial images during transmission and storage, such as using hash values [1][2] instead of real image data.

User Authorization. Ensure compliance with relevant regulations, obtain user consent, clearly inform the purpose of data, and giving users more control, such as whether to participate and how their facial data is used.

Psychological Counseling. Provide user education courses to help them properly understand and use makeup transfer techniques to deal with possible mental health challenges [3][4].

Note that we have found that makeup transfer technology has been widely studied to protect facial privacy [5][6].

[1] *Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. Simultaneous feature learning and hash coding with deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3270-3278, 2015.*

[2] *Cheng Deng, Erkun Yang, Tongliang Liu, Jie Li, Wei Liu, and Dacheng Tao. Unsupervised semantic-preserving adversarial hashing for image search. IEEE transactions on image processing, 28(8):4032-4044, 2019.*


[3] *Juan Luis Higuera-Trujillo, Juan López-Tarruella Maldonado, and Carmen Llinares Millán. Psychological and physiological human responses to simulated and real environments: A comparison between photographs, 360 panoramas, and virtual reality. Applied ergonomics, 65:398-409, 2017.*

[4] *Giuseppina Spano, Annalisa Theodorou, Gerhard Reese, Giuseppe Carrus, Giovanni Sanesi, and Angelo Panno. Virtual nature, psychological and psychophysiological outcomes: A systematic review. Journal of Environmental Psychology, 2023*

[5] *Yuhao Sun, Lingyun Yu, Hongtao Xie, Jiaming Li, and Yongdong Zhang. Diffam: Diffusion-based adversarial makeup transfer for facial privacy protection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24584-24594, 2024.*

[6] *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*

> was data collected legally? resolve contradiction that a previous reviewer discussed

Thank you for your question. Please see our response to another ethical reviewer SJzu.

> was IRB approval received for user study? if not, why? and details for it should be provided in the paper since a table with results is provided

Thank you for your question. We did not apply for IRB approval, mainly due to the following reasons:
1. Risk assessment. During the user study, we only asked the participants to evaluate the generation performance of some competing methods, and did not collected physiological, biological or identity information from any participant. Therefore, we believe that our study poses little risk to participants, and their safety, health and privacy are fully protected. 
2. Participants are fully informed. All the participants are fully informed in advance. They are ensured to fully understand the purpose, process, potential risks and benefits of the study, and voluntarily decide to join our user study.
3. Academic research only. The user study is conducted with an educational background and the related results are only used for academic researches.
4. Publicly accessible data. The training and validation data as well as the competing models used in our user study are all publicly available and accessible.

For these reasons, we did not apply for IRB approval. We will explain in detail in the paper.
> Provide specific details on the data security protocols and anonymization techniques used to protect participant data. This includes information on encryption, data access controls, and storage practices. Clearly state whether informed consent was obtained from individuals whose images are included in the datasets. If consent was not obtained, explain the rationale and any ethical considerations taken into account.

Thank you for your question. 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][4]. The original owners of the data (the authors of the papers) obtained these data by crawling them from the website and provided a Google Cloud link in the publicly available code to access the data. Only the original data owners are able to obtain consent from individuals for these data. We do not know the specific source of each data, so we are unable to obtain consent from individuals. we quote the data collection  process from the original papers.
1. MT dataset from BeautyGAN [5]: " The initial data are crawled from websites. We manually remove low resolution images under bad illumination condition."
2. Wild-MT dataset from PSGAN [6]:" To further prove the effectiveness of PSGAN for handling pose and expression differences, we select images with various pose and expressions from the MT dataset to construct a Wild-MT test set."
3. LADN dataset from LADN [7]: "As a result, we collected our own dataset, starting by collecting high-quality images of faces without occlusion from the Internet."

After downloading the data from the link, we use a dedicated data server with encrypted login to store the downloaded data. Like other existing papers[1][2], we only use these publicly available data to train and test our model. Throughout this process, we do not collect any additional face data. In addition, we do a series of security checks (such as Stable diffusion safety checker and the deep-fake detection) on the generated images to ensure their safety. 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] *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.*

[3] *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.*

[4] *Yuhao Sun, Lingyun Yu, Hongtao Xie, Jiaming Li, and Yongdong Zhang. Diffam: Diffusion-based adversarial makeup transfer for facial privacy protection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24584-24594, 2024.*

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

[6] *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.*

[7] *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.*
Again let me thank the authors for their effort in addressing the concerns!

In summary I am keeping my opinion that ethical concerns are not addressed properly. 

The use of make-up transfer can be use in denigrative ways, for instance, transferring features that are considered shaming in the respective society. For instance, in a gender conservative society, creating make-up with opposite gender features. The checks suggested in the response point to references dealing with identity tracking, and to be honestly, I cannot figure to what (e.g. stable diffusion checker). 
The " license or conform a code of ethics" that will be imposed to uses is, also, un-detailed. The fact that databases are public and popular, in my view, does not solve the license and user agreement problem. Making the code public does not help with ethical problems. As ethical reviewer pointed, it is expected to keep track and secure carefully the use. 

Best regards.
Thank you for your detailed and thoughtful answer! 

Unfortunately, In my view, the contribution to machine learning is not evaluated properly. The evaluation focuses only on the make-up transfer. To stand alone, the contribution should have been evaluated on several different problems. As it is, the paper focuses on the make-up transfer and to address noted limitations, it improves on (machine learning) algorithmic part. Yet the paper does not prove that the improvement goes beyond the approached theme. 

I am keeping my opinion about poor fit with NIPS. Yet this easy to judge by area chairs and program chairs. If this would be ACM-MM, the paper would be fine.

Best regards!
Thanks the authors for providing their training code. Nowadays, only open-sourcing inference code is not healthy for the development of the AIGC field. Given the provided code and other reviewers' comments, though the paper is trying to use LDM to solve makeup transfer which uses many existing techniques (limited novelty), I do appreciate the paper writing and the plotted figures, thus I believe the paper is marginally above the acceptance bar of this conference. I have updated my final rating to 6.
Thank you for your reply. As suggested, we have released the training and inference code of our SHMT method in this anonymous github link: https://anonymous.4open.science/r/SHMT-8754/README.md. Following the steps in README, the users can train their own model from scratch and evaluate it. After the anonymous peer review process, we will also open source an official version of the code and the checkpoint weights of our trained models. We hope that open-sourcing our code will alleviate your concerns and contribute to the development of the related research fields. If you can raise your score after checking our code, we would be very appreciated!
Most of my concerns are addressed. I still don't totally agree with "Pursuing better performance by replacing more powerful backbones is not our main purpose", but I can understand it. I have one concern left. Though I think ethical concern is not a main issue about this paper, if the authors using widely used datasets in existing makeup transfer to train their model, could the authors upload their from-scratch training code in an anonymous github link. I would be glad to raise my score if the authors could open-source the training code for the development of this field.
Please see the Global Author Rebuttal for the response of the ethical concerns.