Face2QR: A Unified Framework for Aesthetic, Face-Preserving, and Scannable QR Code Generation
Authors: Xuehao Cui
Guangyang Wu
Zhenghao Gan
Guangtao Zhai
Xiaohong Liu
Venue: NeurIPS 2024 poster (2024)
Keywords: Image Generation
QR Code
Stable Diffusion
Control Network
Primary area: diffusion_based_models

Abstract: Existing methods to generate aesthetic QR codes, such as image and style transfer techniques, tend to compromise either the visual appeal or the scannability of QR codes when they incorporate human face identity. Addressing these imperfections, we present Face2QR—a novel pipeline specifically designed for generating personalized QR codes that harmoniously blend aesthetics, face identity, and scannability. Our pipeline introduces three innovative components. First, the ID-refined QR integration (IDQR) seamlessly intertwines the background styling with face ID, utilizing a unified SD-based framework with control networks. Second, the ID-aware QR ReShuffle (IDRS) effectively rectifies the conflicts between face IDs and QR patterns, rearranging QR modules to maintain the integrity of facial features without compromising scannability. Lastly, the ID-preserved Scannability Enhancement (IDSE) markedly boosts scanning robustness through latent code optimization, striking a delicate balance between face ID, aesthetic quality and QR functionality. In comprehensive experiments, Face2QR demonstrates remarkable performance, outperforming existing approaches, particularly in preserving facial recognition features within custom QR code designs.

Face2QR: A Unified Framework for Aesthetic, Face-Preserving, and Scannable QR Code Generation
Xuehao Cui', Guangyang Wu", Zhenghao Gan, Guangtao Zhai, Xiaohong Liu' Shanghai Jiao Tong University
fcavosamir, wu. guang guang.young, young, ganzhenghao, zhaiguangtao, xiachongliu)dsjj edu...uuuuuuue.. cn
Abstract
Existing methods generate aesthetic QR codes, such image and style transfer techniques, tend to compromise either the visual appeal or the scannability of QR codes when they incorporate human face identity. Addressing these imperfections, we present Face2QR-a novel pipeline specifically designed for generating person- alized QR codes that harmoniously blend aestheties, face identity, and scannability. Our pipeline introduces three innovative components. First, the ID-refined QR integration (IDQR) seamlessly intertwines the background styling with face ID, utilizing unified Stable Diffusion (SD)-based framework with control networks. Second, the ID-aware QR ReShuffle (IDRS) effectively rectifies the conflicts be- tween face IDs and QR patterns, rearranging QR modules to maintain the integrity of facial features without compromising scannability. Lastly, the ID-preserved Scannability Enhancement (IDSE) markedly boosts scanning robustness through latent code optimization, striking delicate balance between face ID, aesthetic quality and QR functionality. In comprehensive experiments, Face2QR demon- strates remarkable performance, outperforming existing approaches, particularly preserving facial recognition features within custom QR code designs. Codes are available at https: // thub.. com/ cavosamir/FaceeU:
Introduction
Quick Response (QR) codes, due to their capability store substantial amount of data and their ease of accessibility through basic camera devices, have become an exceedingly widespread medium for the representation of information in the digital [13, 47, 3, 4, 36]. With the wide application of QR codes in social context. there has been increasing needs for customizing QR codes to include personal identity and aesthetic allure. However, such needs cannot be fulfilled by the dull appearance of common QR codes, which contain only black and white modules.
With the widespread application of QR codes across diverse fields, related technologies are also developing at a rapid pace. While techniques employing image transformation [5, 6, 13, 46] and style transferring [36, 47] can partially retain facial features, their perceptual quality and aesthetic adaptability are limited. On the other hand, generative model-based approaches [11, 43J can produce QR code images of superior quality and diversity, yet they pose challenges in controlling the generated content, particularly in preserving human facial characteristics. To address these limitations and ensure faithful preservation of face identity within a customized and scannable QR code image, we introduce a novel pipeline, named Face2QR. This approach achieves a balanced compromise between face ID preservation, aesthetic appeal, and scannability for QR code images.
Equal contribution.
Corresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Face images (first row) and QR code images (second row) generated by Face2QR. Our QR codes not only faithfully maintain face ID, but also showcase remarkable scanning resilience and aesthetic quality.
Figure I:
The primary challenges lie in effectively integrating three key aspects: face ID, aesthetic quality, and scanable QR pattern, which can be summarized as follows: (1) Combination of face ID and background. Achieving a harmonious balance between strict facial ID preservation and diverse customized background styles within a unified pipeline presents a notable challenge. Methods reliant on style transfer [36, 47] often yield facial textures that appear unnatural, while those based on image transfer [5, 6, 13, 46] may introduce visible artifacts in the facial region; (2) Conflict between face ID and QR code pattern. While prior generative model-based techniques [43] have demonstrated the ability to control the QR code pattern using QR blueprints, they have struggled to exclude these patterns from the facial region, resulting in unnatural shadows and undesirable artifacts. However, directly removing these patterns from the facial region can make the image unscannable. Thus, achieving balance between maintaining visual quality in the facial region and ensuring the correctness of the QR pattern presents formidable obstacle; (3) Balance between aesthetics and scannability, As revealed [43]. generated images often exhibit a tendency towards being unscannable, necessitating enhancements to their scannability through post-processing. However, globally adjusting brightness can compromise the natural appeal of the facial region. Thus, novel besion-based enhancement methods are worth considering address this challenge.
To address these challenges, the proposed Face2QR pipeline offers a solution for generating person- alized QR codes that strike a balance between aesthetics, facial ID preservation, and scannability. propose ID-refined QR integration (IDQR) to seamlessly combine background and face ID, and ID-aware QR ReShuffle (IDRS) to solve the conflict between face ID and QR code pattern. Specificallyy, IDQR applies a unified SD-based framework to ensure that the generated images have a uniform style. Stable Diffusion (SD) models are guided by two sets of control networks, correspond- ing to face refinement and QR pattern respectively, to achieve separate control in face region and background. IDRS utilizes the flexibility of QR code encoding and reshuffles the modules to make the QR pattern compatible with face ID. Finally, we use ID-preserved Scannability Enhancement (IDSE) to enhance scan robustness through latent code optimization, achieving ID. aesthetics and scannability. Figure shows the QR images generated by Face2QR. It is worth noting that the generated QR images not only the reprints of the provided references, but also have improved aesthetics to align with the generated background, guided by text prompts (e.g., the style and color of clothes have been adjusted accordingly).
trade-off between face
The contributions of this work can be summarized as:
We propose a novel pipeline that holistically integrates aesthetic appealing, facial ID, and scannabil- ity to deliver customized personal representation in QR codes.
We introduce the ID-refined QR integration (IDQR) for seamlessly integrating face ID with background, the ID-aware QR ReShuffle (IDRS) for solving conflicts between face ID and QR pattern, and the ID- ID-preserved Scannability Enhancement (IDSE) for optimizing sean robustness while maintaining face ID and aesthetic quality.
Our Face2QR achieves the State-Of Of- (SOTA) performance in generating the ID-preserved aesthetic QR codes, compared with previous methods.

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2 Related Works
Quick Response (QR) Code. As QR codes emerging as a key connector between real and virtual worlds, there is increasing interest in enhancing the visual appeal of normally monochromatic QR codes. Halftone QR codes [5] offers a design where QR code patterns align with given image in thematically cohesive manner. QRImage and Artup [13, 46] explore ways to encode colorful imagery within a QR code. Other advances [35, 36] have been made in artistic style transfer to increase aesthetic appearance of QR codes. To further customize QR code and obseure overt QR code markers, Chen et al. [2, 4, 23] crafted encoding schemes that consider human visual perception, thus making these patterns less intrusive. TPVM [12] has gone further to conceal QR codes within video content, exploiting the discrepancies in frame capture rates between human vision and digital screens. Similarly, advancements have sought to keep data imperceptible yet accessible through various stealth mechanisms [10, 9, 37, 16, 42, 17].
Diffusion Based Models. Image manipulation and generation techniques powered by deep learning have made strides in recent years [41, 45, 21, 44, 31, 33, 32], with generative models being at the forefront of this development [51, 24, 28, 26]. Novel diffusion-
models such as GLIDE [26], DALLE-2 [28], and Latent Diffusion models [30] have come into prominence. Notably, the Stable Diffusion model [30] moves the denoising steps into the latent dimension a variational autoencoder, which significantly optimizes the generation process in terms of data volume and training time. In parallel, new research has introduced various techniques for modulating the diffusion process. Structural condition interventions have been successfully implemented by ControlNet [52] and T21- Adapter [25]. On a different note, BLIP-Diffusion [20] and SeeCoder [48] have made progress on steering generative outcomes based stylistic aspects.
Identity Preserved Generative Models. In the field of ID ID-preserving image generation, research focuses on maintaining semantic face attributes while generating images that have wide real-world applications. Studies have generally split between techniques requiring test-time fine-tuning, such as Low-Rank Adaptation [15]. and newer optimization- free methods such as FaceO [38], Pho- toMaker [22], and FaceStudio [49], which integrate facial embeddings into the generation process in different ways. While techniques like AP-Aeereer [50] strive for identity consistency by using embeddings from recognition models, they face challenges in compatibility with pre- pre-trained models and ensuring facial fidelity. Most recent work like InstantID [40] use a pluggable module that does not demand fine-tuning and can work seamlessly with available pre-trate pre-trained diffusion models to achieve high-quality face preservation in generated images.
3 Method
The overall structure of Face2QR is shown in Figure 2. The pipeline unfolds through three stages, represented by blue, red and green arrows. Given a user-customized face image f. QR Code m. text prompts and random noise 20, the first stage uses the ID-refined QR integration (IDQR) module to generate an initial QR image 19, The IDQR module includes a pre-trained SDXL model (denoted as SD), an InstantlD [40] network (denoted as Cia) and QR Controller [43] (denoted as Cgr). Stage I can be expressed as: 1 = SD(c, 2o Car (m, e, 20), Cia(S; c, =0)).
(1)
The InstantID network preserves the facial identity information in the generated images, while the QR Controller guides the luminance distribution of the images.
However, as shown in Figure 2, the initial output image from the first stage contains a significant error rate (over 43%). This issue arises from the inherit conflict between two control signals: the foreground face information and the background QR patterns, which are incompatible in the center regions. These conflicts lead to unavoidable QR code errors, presenting core challenge in our pipeline. To address this, we design the ID- aware QR ReSfufe (IDRS) module harmonize these conflicts and regenerate the image using a fine -raiind QR blueprint Ig. As illustrated in Figure 2, this reduces the error rate by more than half. Finally, we use the ID-preserved Scannability Enhancement (IDSE) module to refine the result 1 in latent space, further improving its scanning robustness without compromising the overall visual quality. the following, we introduce the second and third stages in details.
3

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holding ice cream stands in front of colored cupcakes
Reshuffle (IDRS)
Up Sccomsiile
Controller
ID-Refined Integration (IDQR)
IDQR
10
Customize Imputs
Emor 43,85%
IDSE
Segermit Samma
Face2QR
InstandD
Sage
Text Prompts
SDXL
Error: 17.55%
Stage
Stage
ID- Pe-PPreseved Scannihility Enhancement (IDSE)
1
Figure The pipeline of Face2QR is a training-free process for generating ID-consistent and scannable QR code images. Our pipeline has three stages, represented by blue, red, and The IDRS module resolves conflicts between human identity and QR patterns during the control process, while the IDSE module reduces coding errors to ensure the output is scannable.
green arrows.
3.1 ID-Aware QR ReShuffle
As revealed in [43], a fine-grained QR blueprint can effectively control the generator. To resolve control conflicts in the facial region, we design novel blueprint that makes facial information and QR patterns compatible. By leveraging the dynamic characteristics of QR code encoding, we can adaptively rearrange the QR modules. Specifically, maintain the brightness distribution of the facial region and reshuffle the remaining black and white modules accordingly.
First of all, we binarize 1 E B. x3 into module-wise binary information E E RT" By dividing 19 into n modules each of size a, and let 03 be the set of pixel coordinates of the j-th module in 19 the extracted information code E is given by:
[0, if avg (favg(I((0,)) (0)) <T,
E, 1 ar 2T,
(2)
where avg(-) denotes the mean pixel value of the squared patch of size x a. The binarization uses threshold r. typically set to 128 for a total of 256 grayscale levels.
As shown in Figure 3 (left), the binarized QR code is un-scannable due to a significant error rate. To address this, we fix the facial and marker region within E, then rearrange the remaining codes to align with the encoded information. To locate the facial region, we use pre-trained face recognition model to obtain the binary facial mask M, E IR H Let the set A = {j represent the indices of information codes in E that correspond to the facial region, and let the A. represent the indices of marker codes. Our goal is to obtain a new information code E which is partially modified from to make the QR decoder extract lossless information:
= 1}
min (DE (1m)),, s.t.
(3)
E, E,, for j E U
(4)
To ensure the resultant É can be decoded to the target message, aligning with original QR code m, re-generate the error correction code [29] in E.
Afterwards, we expand the binary information of È to image space. We use adaptive-halftone to combine the texture information of I9 with binary code information of É in an adaptive manner, resulting in the blueprint J, E R Hx Note that we leave the facial region unmodified to maintain rich facial features without compromising scanning robustness. The resultant blueprint 4n is then fed

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Un- Scanmabie
Un Seamnele
Scannabee
Binarize
Update
Un-Seanbblle
Fastal
Regzon
Marker
Region
R
Reshulle]
Combine
Scannable
Blueprint
Figure Illustration of IDRS (left) and IDSE (right). In IDRS, we maintain the information codes within the face and marker regions (red and yellow masks) and remap the remaining modules accordingly. In IDSE, we strengthen the finder and alignment pattern, and update latent space using adaptive loss to enhance scannability. Visualization shows the difference between and Ís. Compared with uniform loss, adaptive loss modifies face region more gently.
into SD for the second generation:
Is SD(c, 20 Car (Ib, e, 20), Cia(f, c, 20)).
(5)
Compared with the first generation in Equation 1, both controllers in stage include facial information to mitigate conflicts. As shown in Figure 2, the result of stage 2 reduces errors by more than half compared stage I, while consistently preserving face identity information.
3.2 Scannability Enhancement
The resultant QR image 1 from stage 2 contains a certain QR pattern and consistently reveals face identity, but still unscannable by common QR decoders. In this part, we design the ID- Preserved Scannability Enhancement (IDSE) module to achieve the following two goals: 1) minimize modifications to the QR image (especially for facial region) to ensure scannability: 2) enhance marker region to better harmonize it without compromising scanning robustness. As illustrated in Figure (right), we first strengthen the finder and aligmment pattern I", and then refine using dynamic code loss reach harmonious balance between face ID, visual appeal and scannability.
3.2.1 Marker Harmonziation
The functional regions of QR code, especially the finder and alignment patterns, are crucial for the decoder to locate the QR code. Therefore, these patterns I are strengthened to generate fs. Specifically, for pixel p E 0k where E Am, we have:
(p(p) (p) min( + (),)), if E 1, (p) max(("(p)-7(11 A), if E, 0,
(6)
where À E (0, 1) is hyper-parameter typically set to 0.8 by default. This threshold- enhance- ment helps ensure that the functional regions of the output QR image are easily located.
3.2.2 Spatially Dynamic Loss Function
Inspired by [43], we use gradient descent to update the latent code of f to optimize certain loss function. However, instead of using fixed loss function with constant coefficients, we propose to leverage a spatially dynamic loss function.
Given pretrained VQ-VAE [39] with the encoder & and the decoder D, the optimization is given by:
argmin L(D(z), I+),
(7)
5

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where 2 IR x 4xx4 x4 is the latent code. The loss function L consists of an aesthetic content loss Le and spatially dynamic code loss C.:
2 = argmin (C.(ni))nC(((())).e.n)) (..(,,))))))
(D)()),
(8)
We initialize E(.)), ), and use Adam [19] as the optimizer with learning rate of 0.002 to iteratively update 2 until convergence. Finally, the output _ = (2)) achieves robust scannability and high visual quality.
Adaptive Code Loss. A simulated decoder [36] using a 2D Gaussian kernel can extract module- wise information consistent with common QR decoders. The variance of the Gaussian kernel is key factor in balancing visual quality and scanning robustness. However, in our scenario, we want the facial region to be smooth and the background region to be lossless. Therefore, we propose spatially dynamic code loss. Let Z = D(z), the loss of j-th module is calculated by:
8 = w(j) x avg Is [0,)] o G(j)).
where @ denotes the Hadamard product. G(j) E Raxa is weighting kemel,, and wr(j) is weighting factor defined by:
- Goy: i A w(() = { Wy. ifj E wb: otherwise,
G(1) = ( Gov:
(10)
otherwise
where Go is a 2D Gaussian kernel with variance a. The specific settings for the Wh. of and 0 can be found in the experiments section. Finally, the adaptive code loss is computed by:
wy,
Ce(Z, 16) = Σ w()) x
- To(0,)] o GG)}
(11)
Gaussian distribution with bigger is flatter, which helps equalize the color within the module when updating the latent code. Although this makes modules easier to decode after iterations, bigger a might create unnatural shadow in the face region. On the other hand, Gaussian distribution with smaller a effectively regulates only the central region of a module, making the modules remain unscannable even after updates.
This problem addressed by utilizing adaptive loss for different regions, iee, applying smaller weight wy and o in the face region to prevent distortion on face, and relatively larger wy and of in remaining region to maintain balance between scannability and aesthetic quality.
Aesthetic Content Loss. To ensure the retention of aesthetic qualities while preserving face ID and enhancing scannability, we use the aesthetic content loss to retain essential visual characteristics. quantified by calculating L.= distance [1] (denoted as Dw2) of feature representations between 7 and T as follows:
La (Z,f = Σ
(12)
where Si is feature representations from a pre-trained VGG-19 [34] network layer The aesthetic content loss reflects the global aesthetic quality of the image. By optimizing both code loss and content loss, IDSE module adeptly balances the aesthetic quality, face-preserving, and scannability and creates optimal customized QR code images.
4 Experiments
4.1 Experimental Setup and Configuration
We implemented our pipeline in Python using the PyTorch framework and conducted experiments on an NVIDIA GeForce 4090 GPU. The scannability of QR images is tested using a 27-inch IPS display monitor with refresh rate of 144Hz. In our experiments, we set control strengths for the InstantID network [40] and QR Controller at 0.8 and 1.4, respectively. The parameter À in the marker harmonization process defaults to 0.8. The VAE configuration is consistent with the SD model. The

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Table I: Visual comparison of different methods.
Input
QArt [6]
Halftone [5]
ArtCoder [36]
Tex12QR [43]
Face2QR
0
face recognition model Antelope from InsightFace [14] assists the generation of face mask M in IDRE. The VGG-19 architecture, pre-trained on the MS-COCO dataset, facilitates the feature map extraction in IDSE. The Adam optimizer powers the optimization within IDSE, performing 300 iterations at learning rate of 0.002. Default settings for o, Tb, Wf, and wh are 1.5, 3.0, 1.0, and 15.0 respectively. We produce QR code in version 5, each with 37 x 37 modules. For clarity, define e as the number of error modules in QR image Ie (excluding finder and alignment pattern areas), and e as the number of error modules within the face region. Our dataset for comparative analysis contains 200 uniquely stylized QR images, each 1024 x 1024 pixels in size, with diverse visual content and artistic styles. To more accurately assess the preservation of face identity, we define the face feature distance d as the cosine similarity between the facial features (extracted using ArcFace [7]) of the generated QR image 1 and the original face image f.
4.2 Qualitative Comparison
Aesthetic Quality. In our comparative study, we evaluate our approach against several state-of-the- art aesthetic QR code generation techniques, including QArt [6]. Halftone QR code [5], ArtCoder [36] and Tex12QR [43], as detailed in Table 1. QArt, Halftone QR and Text2QR take the original face image f as the primary input, except that Text2QR takes in additional prompt input c. As ArtCoder is based on neural-style transfer technique, we employ f and 1 to serve as the content reference and the style reference respectively. The results show that Artcoder tends render the texture of style image to face region, causing unwanted distortion on the face. Tex1QQR, on the other hand, cannot preserve face ID due to lack of specific control mechanisms for the face region. In contrast, our QR codes are adept at harmoniously integrating face ID, background and QR pattern, thereby achieving superior visual quality as well as scannability.
Identity Preservation. The comparison between original face image f and the generated image o is shown in Table 2. The face ID is well preserved in the final generated QR image I", with minimal change in haireut or facial expression, which can be further customized by users by adding prompt. The facial region is consistent with the background in style, and the QR pattern is blended seamlessly into the picture. We also compare the generated image / with output of the baseline pipeline Instan- ID [40] in Table 3, which shows that our pipeline achieves similar level of identity preservation as the baseline. The outcomes displayed Table demonstrate that Face2QR consistently generates high-quality images across various poses.
7

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Table 2: Visual comparison of face ID preservation in face image f and generated QR image r If and I3 are generated from fi, and I and Iī are generated from f2. Face feature distance is measured between pairs of 7 and f.
h
Rf
12
f2
13
I
0.53
0.54
0.49
0.51
4.3 Quantitative Comparsion
Scanning Robustness. In this study, we assess the scanning robustness of our QR images using different scanning applications. We first generate a batch of 20 aesthetically pleasing QR codes, each with dimension of 1,024 x 1,024 pixels. These QR images are then displayed on high-definition monitor in three standard sizes: 3cm x 3cm, 5em 5cm, and 7cm x 7cm. During our controlled test, smartphones are held at fixed distance of 25cm from the display, and each code is scanned for 3 seconds from different angles. Over total of 50 trials, the percentage of successful scans is recorded in Table The results reveal an average success rate exceeding 94%, showcasing high reliability of the generated QR images in diverse practical settings. It is also noted that QR images that fail the test in 3s can eventually scanned if given more time. The scanning success rate similar to that of Tex12QR [43], presented in our comparative analysis.
Table 3: Visual comparison of identity preserva-
Table 4: Generated QR images (second row) using face images (first row) with different poses.
tion with InstantID [40].
Input
InstantID [40]
Face2QR
90%
45
0%
Subjective Study. Figure presents a user study consisting of participants to evaluate 150 QR images (50 for each methods) generated by different methods (the approval from Institutional Review Board is obtained). Participants are asked to choose the better one from pair of pictures in the aspect of face ID preservation and aesthetic quality. Each pair of QR images methods using the same face image as input. The percentages represent how many times users prefer the results of method over the other. Our results are preferred by most users.
generated by different
Objective Study. Table 6 shows the statistical performance measured by taking the average of 100 samples. We use the feature distance d, varying from -1 to 1, quantifiable measure for the

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Table 5: Scannability success rates of QR Table 6: codes across various decoders at different sizes and angles.
Comparison of average face feature distance
and average Aesbench scores Ba. [Key: Best]
ArtCoder [36]
Text2QR [43]
Face2QR
Success Rate (%)
0,50
0,43
0.51
Decoders
(3cm)²
(5cm)²
(7em)
Ba
62.0
87.5
90.1
45
90"
45
90
45
90
Face2QR (ours)
Face2QR
ArtCoder
Text2QR
Scanner
100
94
100)
100 100
100
83.5
87.8
89.6
Percentage (%)
78.0
TikTok
100
100
100
100
100
100
WeChat
96
100
100
100
94
100
Text2QR
[43]
Scanner
96
100
100
100
100
16.5
12.2
10.4
22.0
TikTok
100
100
100
100
100
100
Face ID
Aesthetic
Face ID
Aesthetic
WeChat
100 94
100
100
94
100
Figure 4: User study of different methods.
preservation of face ID. The higher the distance d, the generated face is more consistent with the original face image. We also use AesBench tool [8], which assigns aesthetic scores ranging from 0 to 100 (with higher scores denoting better aesthetics), generated pictures. The results indicate that our approach exceeds competing methods in all evaluated metries, confirming its capability to produce QR iamges with faithfully preserved face ID and high aesthetic quality.
objectively evaluate the aesthetic quality of
4.4 Ablation Study
Table 7: IDRE Ablation Study: Compared Table 8: IDSE Ablation Study: We examine the influ- with result obtained by completing the entire ence of wy, wh, of. Wb, ob to the generated QR image stage 2 (rightmost column), skipping stage Jo Model 1 (w
u/; o σb) tends cre- 2 (leftmost column) or conducting stage 2 ate undesirable shadow in the face region. Model without IDRE (middle column) will result in (wy = wh, oy < σp) leads to an increased number more error modules e in the face region. The of error modules e. Model (wy < Wh, of < σb) distribution of error modules, highlighted as reaches a balance between face and scannability. bright areas, is depicted in the second row.
Second zooms in the face region.
19
w/o. IDRE
with IDRE
1
Model 1
Model 2
Model
33
144
137
IDRE Module. Table 7 illustrates how skipping stage 2 or the IDRE module affects the scannability of resultant QR image 7 In stage 2, IDRE first rearranges the QR modules to construct blueprint I in which the QR pattern is compatible with face ID, and then SD model generates I guided by I stage is bypassed, the output 79 from stage is used as the input for stage 3 directly. If IDRE omitted, normal QR code is used to guide the QR pattern generation in stage 2. The result shows that skipping stage 2 or IDRE results substantial increase in the number of error modules ef within the facial area, which significantly reduces the scannability of the QR code.

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IDSE Module. In stage 3, the IDSE module leverages adaptive code loss, which is key to main- taining face identity and simultaneously decreasing number of error modules. This loss function is determined by two parameter pairs ( and σ6 for Gaussian kernel; wf and wy for loss strength). Table presents images produced by the IDSE with varying parameter configurations. The com- parison shows that a uniform leads to distortions in the facial area, and universal loss weight we will increase the number of error modules and compromise the generated image's s scannability, clearer comparison is given by the normalized image difference visualization D shown in Table 9. The visualization demonstrates the module-wise difference between the QR images before and after IDSE. The results demonstrate that the adaptive loss makes the modification in the face region gentler than uniform loss, reaching a better balance between face identity and scannability.
Table Image difference visualization of uniform loss (first row) and adaptive loss (second row).
Table 10: Bad cases caused by failure of generative model.
7
D
face region
Input
Output
5 Conclusion
In summary, we introduce Face2QR, an innovative pipeline that seamlessly integrates face ID, aesthetic design and scannability in the generation of QR codes. By introducing three key modules, iee. IDQR for integrating face ID with aesthetic background, IDRS for resolving conflict between face ID and QR pattern, and IDSE for enhancing scannability while preserving face ID and aesthetic quality, our pipeline is able to balance between three inherently conflicting control signals and generate QR codes that preserve face ID, aesthetic quality and scannability at the same time. Extensive experiments demonstrate that Face2QR significantly outperforms previous methods, establishing new benchmark for generating ID-preserved aesthetic QR codes.
Limitations. Our method is still constrained by some limitations of generative models. Although generative models are powerful, they can produce inconsistent results and often require substantial computing power to generate detailed, high-resolution images. Some typical bad cases caused by failure of generative models are shown Table 10. As these computational models become more advanced, we can anticipate further improvements in the accuracy, speed, and overall aesthetic quality of the generated QR codes.
Broader Impact. By enhancing the visual appeal and personal connection of QR codes, our work has the potential to revolutionize their use in entertainment, social media, marketing, and personal memorabilia, transforming them from mere tools for information transfer into objects of personal expression and aesthetic value. Looking forward, we anticipate that future work will not only refine these methods but also explore their integration into various technological ecosystems, consistently enriching the social and functional aspects of QR codes.
Ack Acknowledgement. The work supported in part by the National Natural Science Foundation of China under Grant 62301310 and 622251 12, and in part by Sichuan Science and Technology Program under Grant 2024NSFSC1426.
10

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A Appendix
A.1 Additional Experiments
A.1.1 Additional Results
Our Face2QR pipeline is generalizable real faces, generated realistic faces, and cartoon faces. The experimental results in Table 11 demonstrate that facial identities are well preserved and seamlessly blended into the background in all generated QR images, showcasing the effectiveness of Face2QR across these three face types.
A.1.2 Visualization of Intermediate Results
In Table 14, we show the intermediate results of Face2QR. Here, /9 represents the output of stage 1, J signifies blueprint image generated by IDRE, and 1 denotes the results of regeneration results from stage 2. The blueprint 1, guides both the generation of Is in IDQR module within stage 2, and the IDSE process in stage generate QR image I with a harmonious balance between face ID. aesthetic quality and scannability. Table 12 presents results from different iterations in the IDSE process. Additionally, Table presents the prompts and models used in the generation of the aforementioned QR image samples.
A.1.3 Loss Curve Running Time
Alagtov (233
Actlleticc [ow(C.))
20000
15000
10000
80
5000
20
130
150
200
250
100
130
2/0
250
300
(ng)
Total tome Mpdolees (c)
400
60
40
15
100
Benutions
Demtiios
Figure 5: Curve of different metrics during IDSE. We show metric curves for diverse samples, each represented by distinct colors. These curves illustrate metric variations over 300 iterations.
In stage 3, we use IDSE to enhance the scannability of 1 by updating the its latent code. The dynamic loss function consists of aesthetic content loss L. and adaptive code loss L.- Both losses apply at the same time and helps the update process to converge sooner. The total number of error module acts as a indicator of scannability, and the error module number in the face region e helps visualize the modification process in the face region. Figure illustrates the above four metrics. The IDSE process converges about 120 seconds when executed on an NVIDIA 4090 GPU to enhance images of size 1024x 1024 pixels.
A.2 Bad Cases
In addition to bad cases shown in Table 10, we present suboptimal cases when the face image and the prompt are conflict with each other Table 13. For example, the first row in Table shows the case when face image of woman and the prompt "A male man" given at the same time.

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A.3 The Interface for User Study
The scoring interface of user study is shown in Figure We adopt the pair-wise comparisons for subjective study rather than absolute ratings since the former is relatively more accurate in general.
Table 11: Real face images of ordinary people (Row 1) collected from [27], generated realistic face (left three Row 3) using StyleGAN2 [18] and cartoon faces (right three on Row 3) with corresponding QR images I (Rows and 4).
User Study: ArtCoder Face2QR
Which onE preserve face identity better?
Right
Which one has better awcchtic quality
Left
Right
Figure 6: The user interface.

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Table 13: Bad I results caused by conflict between face image and prompt.
Table 12: Visualization of IDSE process at different iteration steps.
Input
Iteration 100
Iteration 200
Iteration 300
Input
I
3

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Table 14; Visualization of intermediate results during our aesthetic QR code generation pipeline.
Input
1
o
un-scannable
un-scannable
scannable
un un-scannable
un-scannable
scannable

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Table 15: Prompts for generated QR codes in the paper. All images are generated with size of 1,024x1,024 The generative model is uniformly SDXL Unstable Diffusers YamerMIX.
Sample
Prompt
Figure
"A female woman, face the middle. white bird sitting on a branch of roses, digital
Col
watercolor illustration of meadow with white roses in the morning light, detailed fantastic background of Salvador Dali, waterhouse, Canaletto, watercolor art, intricate, complex contrast, HDR, sharp, soft cinematic volumetric lighting, the background is lost haze. The foreground is brightly lit. 4k"
Figure Col
"A male man, face in the middle. J. R. R. Tolkien-inspired landscape photo, magical landscape inspired by J. R. R. Tolkien The Lord of the Rings, hilly path, bathed a breathtaking play of sunlight splashing surfaces, presents bark textures with color gradients in wood- wood-earth tones, Jungle, mossy rock formations, complicated plants. HDR, Creating photorealistic, asymmetrical composition, complicated details, very detailed, by Greg Rutkowski"
Figure Col 3
"A female woman, face in the middle. olpntng style, ink wash in green and gold tones, Landscape of lotus flowers the foreground over lake, muted colours, wet on wet technique, sketch ink watercolor style with hint of orange and white by Wu Guanzhong, Truong Lo, Mary Jane Ansell, Agnes Cecile, muted splatter art, gold ink splatter, faded dripping paints. green monochrome, soft impressionistic brushstrokes, oil painting, heavy strokes, dripping paint, oil painting, heavy strokes, paint dripping"
Figure Col 4
"A male man, face the middle. flat stylized pine trees, winter landscape with starry night sky and lake, painterly, acrylic painting, trending pixiv fanbox, palette knife and brush strokes, style of makoto shinkai jamie wyeth james gilleard edward hopper greg rutkowski studio ghibli genshin impact"
Figure Col 5
"A female woman, face the middle. (best quality: (intricate emotional details: 1.5), (sharpen details), (ultra detailed), (cinematic lighting), sorcerer's ancient library, ,floating candles, mystical artifacts, magical books, oxfort Key Elements:"
Figure Col 6
"A male man, face in the middle. colorful birds sitting on top of a pink flower, fantasy, parrot by Adam Marczy ski, fantasy art, art of alessandro pautasso, glowing oil delaiee beautiful animals, artwork in the style of guweiz"
Table Row 1
"A male man, face in the middle. painted clouds and landscape background, Watercolor, trending on artstation, sharp focus, studio photo, intricate details, highly detailed, by greg rutkowski"
Table 1
"A female woman, face in the middle. UHD, (masterpiece) Landscape of the Great Wall of China, smoke effects, trending artstation, sharp focus, intricate details, highly detailed,"
Row 2
Table 1
"A male man, face in the middle. A ocean pastel pink blue and lilac ice cream, with a boat made of candy, waves"
Row 3
Table 2 Col
"A male man, face in the middle. Hatsune Mecha Tech Sense HD Wallpaper, ultra hd, realistic, vivid colors, highly detailed, UHD drawing, pen and ink, perfect composition, beautiful detailed intricate insanely detailed octane render trending artstation, 8k artistic photography, photorealistic concept art, soft natural volumetric cinematic perfect light"
Table 2 Col
"A male man, face in the middle. in the style of james gilleard, SamDoesArts, art by Sam Yang. absolute beauty birth'd 'd from fragile chaos, mandelbulb dress, insanely detailed, full of life, animated"
Table 2 Col
"A male man, face in the middle. impressionist landscape of Japanese garden in winter with a bridge over a pond"
Table 14
"A female woman, face in the middle. Highly detailed beautiful landscape, vintage style, bright colors, atmospheric lighting flowers. einematic composition, digital painting, elegant, beautiful, high detail, by Willem Haenraets, trending on artstation, sharp focus, studio photo, intricate details, highly detailed, by greg rutkowski"
Row 7&8
5

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12


Reviews:
[{"review_id": "pK1J9LXjsJ", "reviewer": "Reviewer_p8m4", "summary": "The paper presents an interesting framework to generate face-preserving QR code, which is useful in social entertainment applications. To enable this application, the paper first encode the Face ID information into the QR generation process and a refining process is applied to improve the integrity of facial features as well as the scannability of the QR code. Experiments results show the effective of the proposed algorithm. The paper is well presented and would be easy to reproduce.", "questions": "The main concern is on the novelty of the paper. Please well justify the novelties of the paper.", "limitations": "The paper has discussed the potential limitations in the Conclusion section.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed face ID preserved QR code generation is useful in industry applications. \n2. The presentation of the paper is clear and easy to follow. There are sufficient details to reproduce the paper.", "weaknesses": "1. The paper seems to be an engineering report. The novelty of the paper is limited and it seems more likely to be an application paper rather than a neurips submission. \n\n2. For the experimental evaluations, there are several points which should be well improved.\n* The evaluation test set is relatively small. For example, for scanning robustness test, there are only 20 QR codes for the test, which may not be statistically effectively. \n* For the ID-preserving results, it seems the ID results have been compromised compared with the original images. \n* Also, the generated face results seems not be consistent with the QR codes, as shown in Figure 1 and Table 1. It seems to be more like a simple combination of a QR code with a face image.\n\n3. The illustrative results are based on the celebrities or movie stars. How about the results to common face?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer p8m4,\n\nThank you for taking the time to review our manuscript and providing valuable feedback. All raised concerns are addressed below point by point:\n> **Q1: [The paper is more likely to be an application paper rather than a neurips submission.]**\n* We believe that our Face2QR is novel since it is pioneering in the field of image-to-image generation to generate QR codes that preserve face ID, scannability and aesthetic quality at the same time. The pipeline is able to balance between three inherently conflicting control signals and achieve the SOTA performance. More specifically, the IDQR module preserves the facial identity in the generated image aligned with text prompts, and ensures the luminance distrbution matches that of a QR code. The IDRS module uses face masks to preserve the fidelity of face ID and resolves conflict between face ID and QR patterns by reshuffling the QR code to match the brightness distribution in the face region. The IDSE module iteratively updates the generated image in the latent space and applies adaptive loss to carefully preserve the face ID while enhancing scannability. We will make these contributions clearer in the revised manuscript.\n\n* The innovation of our work not only lies in the modules of our model but also lies in the proposed **training-free framework (no parameters updated)** for solving complex control problem in which triplet control signals inherently conflict with each other. We believe that this new framework will facilitate the subsequent research in community about the effective control of generative models.\n\n> **Q2.1: [The evaluation test set is relatively small.]**\n* This paper adopts setting of scanning robustness test from previous works [41,34], which also use a batch of 20 samples. We conducted a scanning robustness experiment with an expanded test set of 100 QR codes, using the same settings as described in the manuscript. The test results of our Face2QR are shown in the table below, with an average successful rate over 95%. The successful rate of this new scanning robustness experiment is consistent with that reported in the manuscript.\n\n| Decoder | $(3\\text{cm})^2$@$45^{\\circ}$ | $(3\\text{cm})^2$@$90^{\\circ}$ | $(5\\text{cm})^2$@$45^{\\circ}$ | $(5\\text{cm})^2$@$90^{\\circ}$ | $(7\\text{cm})^2$@$45^{\\circ}$ | $(7\\text{cm})^2$@$90^{\\circ}$ |\n| ------- | ---------------: | ----------: | ---------------: | -------------------------------: | -------------------------------: | -------------------------------: |\n| Scanner |                              98% |                              96% |                             100% |                             100% |                              99% |                             100% |\n| WeChat  |                              95% |                              99% |                             100% |                             100% |                              98% |                              98% |\n| TikTok  |                             100% |                             100% |                             100% |                             100% |                             100% |                             100% |\n\n> **Q2.2: [The ID results have been compromised compared with the original images.]**\n\n* In our pipeline, the InstantID [38] network is used to preserve face identity during the generation process. We compare our generation results with the results of InstantID using the same prompt in Table A of the PDF. Compared with the baseline of InstantID, our results with additional QR information show little degradation in quality of face identity. For the generated QR images to be practical in daily life, they must be successfully decoded by standard QR code decoders originally designed for black and white QR codes. Therefore, although the balance between face identity and QR pattern is carefully managed, subtle artifacts, such as color blocks in the face region, may still occur due to the compromises made for scanability. If a decoder is designed specifically for decoding aesthetic QR codes, we believe it can completely eliminate the impact of QR patterns on faces. In the current situation, our method is likely the best solution for balancing face identity, QR code patterns, and aesthetics.\n\n> **Q2.3: [The face results seems not be consistent with the QR codes. The results are like a simple combination of a QR code with a face image.]**\n\n\n* In the generated QR image, the face ID is consistent with the original face image. Moreover, the decoded QR code matches the original encoding, indicating the functional consistency between the face region and the QR pattern.\n* We do not consider our generated QR images to be simple combinations of a QR code with a face image. Indeed, conventional methods such as ArtUp [44] attempted to combine them directly, but this often comes at the expense of aesthetic quality. As shown in Table B of the PDF, ArtUp directly pastes the user-provided face image onto the QR code, resulting in much lower aesthetic quality compared to ours. By harmonizing triplet control signals, our Face2QR successfully achieve superiority in balancing aesthetics, face-preserving, and scanability. It is worth noting that the clothes, pose, hairstyle, and other features in the generated images have been adjusted accordingly to achieve holistic semantic consistency, far beyond only combining a QR code with a face image.\n\n> **Q3: [How about the results to common face?]**\n\n* Our Face2QR is generalizable to real common faces, generated realistic faces, and even cartoon faces. The experimental results are shown in Table C of the PDF. It can be seen that the face identities, no matter which type it is, are well preserved in the generated QR images with seamlessly blending into the background, which demonstrates the effectiveness of Face2QR in these three face types.\n\nThanks again for your review. We hope our response has well addressed all your concerns.", "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": "LLTKgltIzN", "reviewer": "Reviewer_ebKp", "summary": "This work proposed Face2QR, a pipeline for generating personalized QR codes that balance aesthetics, face identity, and scannability. It mainly introduces three components: ID-refined QR integration (IDQR) for seamless background styling with face ID, ID-aware QR ReShuffle (IDRS) to rectify conflicts between face IDs and QR patterns, and ID-preserved Scannability Enhancement (IDSE) to boost scanning robustness through latent code optimization. Face2QR outperforms existing methods in preserving facial recognition features within custom QR code designs.", "questions": "Please refer to details in the Weakness section above.", "limitations": "Some limitations have been discussed in the submission.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ Generating personalized QR codes that balance aesthetics, face identity, and scannability seems an interesting topic and application. \n\n+ The design of the major components in this paper, including IDQR for seamless background styling with face ID, DRS to rectify conflicts between face IDs and QR patterns, and IDSE to boost scanning robustness through latent code optimization, is well-motivated and reasonable.\n\n+ Experiments with user studies show the proposed method has strength compared with existing method like Text2QR, ArtCoder etc.", "weaknesses": "- The proposed method is mainly build upon existing works in areas like Diffusion models, Identity Preserved Generative Models, and latest QR generation method like [41]. It is indeed a good application work but may lack technical contributions. \n\n- The proposed new components are good practice for this specific application about QR code generation. It is a good application paper but I'm not sure whether these insights are general enough to fit a requirement for a NeurIPS paper. For example, it is not clear if the proposed method can generate impact or useful for more general topics such as Identity Preserved Generative Models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer ebKp,\n\nThank you for taking the time to review our manuscript and providing valuable feedback. All raised concerns are addressed below point by point:\n> **Q1: [Method is built upon existing work like Diffusion models, Identity Preserved Generative Models and latest QR generation method like [41].]**\n\n* While Diffusion models, Identity Preserved Generative Models, and latest QR generation method are components of our framework, the key technical contributions lie in the three modules (i.e., IDQR, IDRS, and IDSE), which integrate these components and balance the three control signals. More specifically, the IDQR module integrates a face ID with an aesthetic background, the IDRS module harmonizes the QR pattern with the face ID, and the IDSE module balances between scannability and aesthetic quality. In Table 1, Text2QR [41] shows the unsatisfactory results without harmonizing the face ID with QR pattern and the background. More importantly, our contribution also lies in the proposed **training-free framework** for solving **complex control problem** in which triplet control signals inherently conflict with each other. We believe this new framework could facilitate the subsequent research in community about the effective control of generative models.\n\n> **Q2: [Whether these insights are general enough? Will the proposed method generate impact for more general topics?]**\n\n\n* Since the proposed framework resolves the conflicts among triplet control signals, it is applicable to other generation tasks involving multiple controls. For example, with some modifications to the pipeline, it is feasible to control face identity, object and background simultaneously. One possible solution is to have one module integrate face identity with background, another harmonizes object positions with face identity, and the final module balance the generation of objects and background. Additionally, this framework can be also applied to other tasks, such as generating videos that preserves the motion of a designated person across various backgrounds. Therefore, although our Face2QR is specifically designed for QR code generation, the built training-free framework with triplet controls provides valuable insights and has broader impacts across various fields, especially in the control of generative models.\n\nThanks again for your review. We hope our response has well addressed all your concerns.", "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": "MXInv3LdNF", "reviewer": "Reviewer_MYMo", "summary": "The article introduces a novel pipeline designed to create customized QR codes that integrate aesthetic appeal, facial identification (ID), and scannability. The proposed approach incorporates three key components: (1) ID-refined QR Integration (IDQR) seamlessly incorporates facial ID into the QR code background (2) ID-aware QR ReShuffle (IDRS) addresses and resolves conflicts between facial ID and QR code patterns (3) ID-preserved Scannability Enhancement (IDSE) optimizes the robustness of QR code scanning while preserving both the facial ID and aesthetic quality.", "questions": "Quantitative and qualitative experiments on a larger and more diverse group of ordinary users are crucial to demonstrate the scalability and effectiveness of the method.", "limitations": "The authors adequately addressed the limitations of their work", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The motivation of Face2QR proposed in the paper is straightforward, and the method proves to be effective based on the quantitative and qualitative results presented.\n(2) There is sufficient ablation study to demonstrate the effectiveness of each module in this paper.\n(3) This paper is well-written and easy to follow.", "weaknesses": "(1) The paper's innovation is relatively weak, with each module and its technology being a combination of previous works.\n(2) Although the paper includes numerous quantitative experiments, it only involves up to 20 identities, all of whom are celebrities. This limitation hinders the ability to fully demonstrate the method's effectiveness for a broader range of ordinary users.\n(3)The paper does not mention how to set the value of a in line 131.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer MYMo,\n\nThank you for taking the time to review our manuscript and providing valuable feedback. All raised concerns are addressed below point by point:\n\n> **Q1: [Each module and its technology is a combination of previous works.]**\n* We acknowledge that the components—Diffusion models, Identity Preserved Generative Models, and the latest QR generation methods—are directly used. The reason for this design is to allow new, better methods to be easily plugged into our proposed **training-free framework**. Our innovation lies in how we control these components, which is why we proposed the three modules: IDQR, IDRS, and IDSE.\nThe key technical contributions of our work are embodied in these three modules, which integrate the components and balance the three control signals. Specifically:\n\n  - IDQR Module: This module integrates a face ID with an aesthetic background. It preserves the facial identity in the generated image aligned with text prompts, ensuring the luminance distribution matches that of a QR code.\n  - IDRS Module: This module harmonizes the QR pattern with the face ID. It uses face masks to preserve the fidelity of the face ID and resolves conflicts between the face ID and QR patterns by reshuffling the QR code to match the brightness distribution in the face region.\n  - IDSE Module: This module balances scannability and aesthetic quality. It iteratively updates the generated image in the latent space and applies adaptive loss to carefully preserve the face ID while enhancing scannability.\n\n  In Table 1, Text2QR [41] demonstrates unsatisfactory results due to the lack of harmony between the face ID, QR pattern, and background. In summary, the innovation of our work lies not only in the modules of our model but also in the proposed training-free framework for solving complex control problems, where triplet control signals inherently conflict with each other. We believe that this new framework will facilitate subsequent research in the community on the effective control of generative models. We will clarify these contributions in the revised manuscript.\n\n> **Q2: [Involved identities are all celebrities. How about the method's effectiveness for a broader range of ordinary users?]**\n* Our Face2QR system is generalizable to real faces, generated realistic faces, and cartoon faces. As shown in Table C of the PDF, the experimental results demonstrate that facial identities are well preserved and seamlessly blended into the background in all generated QR images, showcasing the effectiveness of Face2QR across these three face types.\n\n> **Q3: [The paper does not mention how to set the value of a in line 131.]**\n* The value $a$ is the pixel length of one module, which is determined by the QR image size and the version of the QR code. In our experiments, the version 5 of QR code is leveraged, so there are typically $37\\times 37$ modules in a QR code image. Then for an QR image has size $L\\times L$, the value of $a$ should be $L/37$. Such details will be included in the revised manuscript.\n\nThanks again for your review. We hope our response has well addressed all your concerns.", "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": "5s7PYkOOcG", "reviewer": "Reviewer_jWT5", "summary": "This paper is pioneering in its integration of face identity with QR codes, proposing a novel pipeline for generating customized QR codes with embedded face identity. The key idea is to leverage diffusion models and control networks to create visually appealing QR codes while preserving face identity. The pipeline introduces an ID-aware QR ReShuffle module to address conflicts between face identity and QR code patterns, and designs an ID-preserved Scannability Enhancement module to improve scannability without compromising the face identity and visual quality. The experiment results showcase a perfect balance between face identity, aesthetic quality and scannability.", "questions": "- Can the authors provide anaylsis of bad cases caused by failure of generative models?\n\n- Could the authors provide definition of error rate and comparison of image difference visualization $D$ between adaptive and uniform losses?\n\n- While the method excels in visual quality, what are the computational resource requirements compared to other methods?", "limitations": "Yes. The paper adequately discusses limitations and broader impact.", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- As the first paper to combine face identity with QR codes, the proposed pipeline for generating face embedded QR code is innovative and addresses the practical needs for social connection in real-world scenarios.\n\n- The IDRS module presents an interesting solution to conflicts arising from different control signals. By rearranging QR patterns to harmonize varying control conditions, the proposed pipeline leverages information from both face images and QR codes to generate customized QR codes.\n\n- The IDSE module significantly enhances the scannability of QR images using adaptive loss, while concurrently maintaining a faithful representation of face identity.\n\n- Experimental results show that the generated QR codes successfully preserve face identity, yielding impressive visual results. Notably, there is minimal interference from QR patterns in the face region.\n\n- The paper is well-organized, thoroughly discussing motivation and related work. Rigorous experiments enhance the credibility of the results.", "weaknesses": "- The paper mentions that the method is limited by the generative models, but does not present bad cases due to failure of generative models. It is recommended to include such examples to provide readers with a better understanding of the algorithm’s limitation.\n\n- Certain technical details in the paper require further elaboration. For example, definition of error rate is not explictly given. In Figure 3, the image difference visualization $D$ appears to support the claim that “adaptive loss modifies face region more gently”. However, without a comparison of $D$ between adaptive and uniform losses, this claim lacks substantiation.\n\n- The paper lacks a comparison of computational resource requirements with other methods. This omission makes it challenging to assess the practical feasibility of this algorithm.\n\n- Typos exist; for instance, line 174 should read “with a learning rate of 0.002”", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer jWT5,\n\nThank you for taking the time to review our paper and providing valuable feedback. Below, I will address the raised concerns:\n\n> **Q1: [Can the authors provide anaylsis of bad cases caused by failure of generative models?]**\n* We include some bad cases caused by failure of generative model in Table D of the PDF. This issue may arise from the lack of diversity in the training data, or the model’s inability to generate complex structures or understand nuanced prompts. We hope these examples can provide a clearer understanding of the algorithm’s limitations. Notably, Face2QR is designed to allow new, better generative models to be easily plugged into our proposed training-free framework. As generative models advance, we expect ongoing improvements to address these limitations, increasing the robustness and versatility of future models. Our framework is adaptable and will benefit from these advancements, helping us to push the boundaries of what generative models can achieve.\n\n> **Q2: [Could the authors provide definition of error rate and comparison of image difference visualization $D$ between adaptive and uniform losses?]**\n* The error rate is defined as $e/N_\\theta$ where $e$ is the number of error modules and $N_\\theta$ is the total number of modules in a QR code excluding the marker and alignment pattern region. In our experiments, the generated QR images have version 5 and $37\\times 37$ modules in total. Therefore, the value of $N_\\theta$ is typically $1197 = 37^2-3\\times 7^2-5^2$.\n* In Figure 3, the visualization $D$ primarily illustrates the differences in modification between the face region and the background when using the adaptive loss. We are pleased to provide the comparison of image difference visualization $D$ between adaptive and uniform losses in Table E of the PDF. It is shown that the modifications in the face region are less pronounced with adaptive loss. A more straightforward comparison between adaptive loss and uniform loss can be found in Table 7, where we demonstrate how different losses affect the nuance of face ID.\n\n> **Q3: [What are the computational resource requirements compared to other methods?]**\n* Face2QR is able to run on one RTX 4090 GPU, so our pipeline has similar computational resource requirements as previous method such as Text2QR [41] and ArtCoder [34]. In the first two modules (IDQR and IDRS), the pipeline goes through Stable Diffusion twice, and the latent code update in the last module (IDSE) can converge within 150 iterations. In terms of generation time, Face2QR outputs a QR image in about five minutes on one RTX 4090 GPU, which is about the same as Text2QR.\n\n> **Q4: [Typo exists on line 174.]**\n* The typos in the article will be addressed accordingly. Thanks for pointing them out.\n\nThanks again for your review. We hope our response has well answered your questions.", "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:
Dear Reviewers and Area Chairs,

We appreciate the reviewers (**R1** p8m4, **R2** ebKp, **R3** MYMo, and **R4** jWT5) for their insightful feedback.
The reviewers agree that:

**Novel approach**:
* **R3**: "The article introduces a **novel** pipeline designed to create customized QR codes..."
* **R4**: "As **the first paper** to combine face identity with QR codes, the proposed pipeline for generating face embedded QR code is **innovative**..."

**Effectiveness**:
* **R1**: "Experiments results show the **effective** of the proposed algorithm."
* **R2**: "Face2QR **outperforms existing methods** in preserving facial recognition features within custom QR code designs."
* **R3**: "There is sufficient ablation study to demonstrate the **effectiveness** of each module in this paper."
* **R4**: "Experimental results show that the generated QR codes **successfully preserve face identity**, yielding **impressive visual results**."

**Interesting**:
* **R1**: "The paper presents an **interesting** framework..."
* **R2**: "Generating personalized QR codes that balance aesthetics, face identity, and scannability seems an **interesting** topic and application."
* **R4**: "The IDRS module presents an **interesting** solution to conflicts arising from different control signals."

**Well-Written and Organized**:
* **R1**: "The paper is **well presented** and would be **easy to reproduce**."
* **R2**: "The design of the major components in this paper, including ..., is **well-motivated** and **reasonable**."
* **R3**: "This paper is **well-written** and **easy to follow**."
* **R4**: "The paper is **well-organized**, thoroughly discussing motivation and related work."

We have responded individually to each reviewer to address any concerns.

Best Regards,

Authors

**References for the PDF file:**

[51] Pexels. Accessed: 2024-08-06.

[52] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of StyleGAN. In Proc. CVPR, 2020.

Meta-review: This work proposed a interesting pipeline, Face2QR, for generating personalized QR codes that balance aesthetics, face identity, and scannability. After rebuttal, the paper receives unanimous accept from all reviewers. All reviewers agree that the paper is well-written and the experimental results are impressive and the proposed method is interesting.
Decision: Accept (poster)