AnimateQR: Bridging Aesthetics and Functionality in Dynamic QR Code Generation
Authors: Guangyang Wu
Huayu Zheng
Siqi Luo
Guangtao Zhai
Xiaohong Liu
Venue: NeurIPS 2025 poster ()
Keywords: ControlNet
AnimateDiff
diffusion
QR code
Primary area: applications
TL;DR: The first animated QR code generation method.

Abstract: Animated QR codes present an exciting frontier for dynamic content delivery and digital interaction. However, despite their potential, there has been no prior work focusing on the generation of animated QR codes that are both visually appealing and universally scannable. In this paper, we introduce AnimateQR, **the first generative framework** for creating **animated QR codes** that balance aesthetic flexibility with scannability. Unlike previous methods that focus on static QR codes, AnimateQR leverages **hierarchical luminance guidance** and **progressive spatiotemporal control** to produce high-quality dynamic QR codes. Our first innovation is a multi-scale hierarchical control signal that adjusts luminance across different spatial scales, ensuring that the QR code remains decodable while allowing for artistic expression.  The second innovation is a progressive control mechanism that dynamically adjusts spatiotemporal guidance throughout the diffusion denoising steps, enabling fine-grained balance between visual quality and scannability. Extensive experimental results demonstrate that AnimateQR achieves state-of-the-art performance in both decoding success rates (96\% vs. 56\% baseline) and visual quality (user preference: 7.2 vs. 2.3 on a 10-point scale). Codes are availble at https://github.com/mulns/AnimateQR.

AnimateQR: Bridging Aesthetics and Functionality in Dynamic QR Code Generation
Guangyang Wu, Huayu Zheng, Siqi Luo, Guangtao Zhai, Xiaohong Liu"
Shanghai Jiao Tong University
ww..uuang.young zhenghuayu, siqilu0647, zhaiguangtao, xiaohongliu @sjtu.edu.cn
Abstract
Animated QR codes present exciting frontier for dynamic content delivery and digital interaction. However, despite their potential, there has been no prior work focusing the generation of animated QR codes that are both visually appeal- ing and universally scannable. In this paper. we introduce AnimateQR, the first generative framework for creating animated QR codes that balance aesthetic flexibility with scannability. Unlike previous methods that focus on static QR codes, AnimateQR leverages hierarchical luminance guidance and progressive spatiotemporal control to produce high-quality dynamic QR codes. Our first innovation is multi-scale hierarchical control signal that adjusts luminance across different spatial scales, ensuring that the QR code remains decodable while al- lowing for artistic expression. The second innovation mechanism that dynamically adjusts spatiotemporal guidance throughout the dif- fusion denoising steps, enabling fine-grained balance between visual quality and scannability. Extensive experimental results demonstrate that AnimateQR achieves state-of-the-arr performance both decoding success rates (96% vs. 56% baseline) and visual quality (user preference: 7.2 vs. 2.3 on a 10-point scale). Codes are availble at https: // thub. www/mulns////ccc/mmcmwccwccccltm An AmimatQQR.
progressive control
Introduction
Aesthetic QR codes have emerged as a promising medium that integrates machine-readable func- tionality with human-oriented visual design [41, 31, 22, 14]. Early approaches typically focused on module deformation [3, 10, 45, 4, 16] and style transfer [41, 41] to improve visual appeal. With the advancement of generative models [32, 36, 15, 35, 17, 19, 18, 20, 26, 28, 27], recent methods employ ControlNet-be bes-baed frameworks [6, 43] to synthesize stylized QR code images [34]. To further ensure scanning reliability, dedicated mechanisms [22, 34, 39, 5] have been introduced to balance the trade-off between aesthetics and robustness.
Unlike existing static QR codes, animated QR codes encoded as video sequences offer enhanced branding potential, interactive storytelling, and context-aware content delivery. However, generating temporally coherent animated QR codes that maintain robust scannability while achieving artistic expressiveness presents significant challenges.
To the best of our knowledge, we are the first to tackle animated QR code generation, addressing critical gap in existing methods that are primarily designed for static image domains. Directly applying these static methods [4, 3, 22, 34, 39] to animated scenario in frame- coane-by-frame manner results in visually unappealing outcomes, often producing rigid and unnatural animations due to lack of temporal coherence and aesthetic consistency. Our motivation arises from the observation that overly fine-grained control leads to instability, whereas overly coarse control introduces visual
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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O
Figure I: Samples of animated QR codes are shown. Best viewed with Acrobat Reader. Click the images play the animation clips and zoom in for finer details.
artifacts. To balance robustness and visual quality, we propose novel framework with adaptive control granularity, enabling dynamic adjustment of spatial and temporal constraints to prioritize scannability in critical regions while allowing creative freedom less important areas. We name the method AnimateQR, which introduces two key innovations:
1. Hierarchical Luminance Guidance (HLG). HLG is multi-scale luminance map that can be derived from both natural images and QR codes. It encodes hierarchical structural information, serving control mechanism to bridge artistic expression and QR code functionality. Our proposed HI M..G-CnntolNNet, trained on natural images and their corresponding HIG maps, achieves superior performance than existing luminance ControlNet [43] through three key innovations:
(I) Module-aligned Encoding: Spatial partitioning aligns with QR decoder's module-wise processing requirements, ensuring structural compatibility. (2) Hierarchical Control: Multi-scale constraint mechanism enables dynamic granularity adaptation across different QR module hierarchies. (3) Adap- tive Constraint: Temary quantization strategy selectively preserves critical regions while allowing flexibility in non-essential areas through stochastic relaxation.
2. Progressive SpatioTemppras control (ProST). Building upon the HL.G-ControlNet framework, we propose a novel diffusion-based progressive spatiotemporal control mechanism that jointly optimizes visual quality and scannability during the inference phase. Unlike conventional approaches that enforce uniform control across frames, ProST dynamically partitions control strength across both spatial and temporal dimensions:
Temporally: ProST assigns distinct HLG maps each frame through a combination of reshuffling and interpolation, ensuring temporal coherence while maintaining scannability.
Spatially, the HLG control signals adaptively evolve across diffusion stages: regions prone to decoding errors receive stronger guidance, while more stable areas transition to softer constraints.
This dual-domain adaptation mechanism effectively balances video coherence with decoding reliabil- ity, achieving an optimal trade-off between visual appeal and functional robustness.
Extensive experimental results demonstrate that AnimateQR achieves state-of-the-art performance in both decoding success rates (96% vs. 56% baseline) and visual quality (user preference: 7.2 vs. 2.3 on a 10- 10-point scale).
The key contributions of this paper threefold: 1) First generative framework for animated QR codes. 2) Hierarchical Luminance Guidance (HLG) that enhances control granularity spatially. 3) Progressive Spatiotemporal Control (ProST): diffusion-based method for error-adaptive control across space and time.
2 Related Work
Quick Response (QR) Code. As QR codes increasingly serve as a vital bridge between physical and digital domains, there is growing interest in enhancing their visual aesthetics beyond the tradi- tional monochrome design. Early efforts, such as halftone QR codes [3], align QR modules with thematic images to create visually integrated patterns. Artup [7, 40] further explore the embedding of colorful content within QR structures. For module rearrangement, the QArt team [4, 16] pioneered the application of Gaussian-Jordan elimination algorithms to spatially reorganize encoding units. Subsequent advances [30, 31] apply artistic style transfer techniques enrich QR code appearance.

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HIG Map Extraction (Hierarchical Luminance Guidanee)
-10
HLGG-ontrooNet -Training
Isput Inaeee imagee
Ma
Multi-scale Central Rinary Vlask
7
(-10,11
[0.1]
HI.G Map
HEG Map (H)
Extraction
YE YE
ArR
X0
Ternary quandioer
X0
ndd
20
Yk
Conteelca
Activation Vector:
P
BE
B
add nouse
Progressive Spatiotemporal Control Strategy (ProST) Inference
Stuble Diflision
QR Code
Init HLG
Evolve HLG
Evolve HIG
Scamubiilty Enhancenenn
Compune
x
Propogation
Loss
H
11
Random Noise
Prompt: girl, night city, coot,
pachets
DDIM
Figure 2: Illustration of the proposed AnimateQR framework. The green box depicts the extraction of the Hierarchical Luminance Guidance (HLG) map from input images. The red box outlines the training of the Ho.toContr,,,ee,, while the blue box highlights the inference stage using proposed Progressive Spatiotemporal control (ProST) strategy. During training, we extract HLG map with randomly sampled activation vector 3x as detailed in Section 3.3. During inference, animated QR codes are generated via the DDIM process using Stable Diffusion model equipped with HLG--CotrooNeet (as the control unit) and AnimateDiff (as the motion module). The HLG map is progressively updated by the Evolve HLG module as detailed in Section 3.4, enabling adaptive spatiotemporal control throughout the denoising process.
Style transfer technology further elevates the artistic quality of QR codes. Xu et al. [41] proposed SEE QR codes, marking the advent of aesthetically pleasing QR codes based on style transfer. Subsequently, Su et al. [30, 31] enhanced the aesthetic effect through module-based deformable convolution mechanism (MDCM), generating QR codes that are both recognizable by ordinary mobile scanners and artistically expressive.
Diffusion for Aesthetic QR Image: In recent years, deep learning- based image manipulation
and generation techniques have made significant progress [32, 36, 15, 35, 17, 19, 18, 20], with
generative models leading the forefront of this advancement [42, 21, 24, 23, 26, 28, 27]. QRBTF [22]
employs diffusion models [25] to generate photorealistic QR codes with natural textures while maintaining robust scannability. Meanwhile, GladCoder [39] integrates GANs [8] and diffusion models [25] refine high-frequency details in QR codes, enabling them mimic complex artworks without sacrificing machine readability, thus solving the problem of artistic expression limitations in traditional QR designs. The QRMonster model [6]. built upon the ControlNet framework. enhances structural control capabilities of diffusion models through its adapter module [46]. Text2QR [34] leverages QRMonster to improve visual output quality while optimizing positioning patterns via three-stage refinement process, thereby significantly enhancing the overall aesthetic quality of QR code images. Face2QR [5] represents another noteworthy research direction, which explores the integration of facial features into QR code design to create unique and highly personalized solutions. This innovation undoubtedly opens up new possibilities for the future of QR code aesthetics.
Method
3.1 Preliminary
QR code is decoded through three primary steps:
1. Detection and Localization: Locate the QR code region by detecting Finder Patterns (the square markers at three corners) and Alignment Patterns, and determine the QR code's version and format information.

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2. Module Sampling and Binarization: Partition the QR code image into grid of m x m modules, where each module assigned binary value (0 or 1) based on its luminance level.
3. Error Correction and Decoding: Decode the binary sequence using Reed-Solomon error correction retrieve the original message.
In this paper, we define all QR code images as composed of m m modules, where each module consists of x pixels. For convenience, we standardize all images to size of ma ma.
3.2 Overall Framework
The overall framework of our approach is illustrated in Figure 2. During inference, our method follows the standard Denoising Diffusion Implicit Models (DDIM) [29] process to denoise a randomly sampled noise using Stable Diffusion (v1.5) [25]. Additionally, we employ ControlNet [43] for spatial luminance control and DifmateDiff [9] for motion generation in video sequences. Specifically, the ControlNet we utilize adheres to the standard model architecture but trained on novel Hierarchical Luminance Guidance (HLG) signal dataset, referred to as H..G-ControNeet. During the denoising process, we dynamically adjust the control signals through mechanism named Progressive Spatiotemporal Control (ProST), assigning varying control strengths both spatially and temporally to achieve a fine-grained balance between visual quality and scanning robustness.
The rationale for selecting AnimateDiff as the motion module can be summarized as follows: (1) An- imateDiff seamlessly integrated with the image generation model Stable Diffusion, which benefits from a rich ecosystem of community fine-tuned stylized models. (2) By leveraging AnimateDiff, we can apply control through image- based ControlNet without the need train a separate video Control- Net. This design makes our method highly extensible, enabling both static and animated QR code generation with minimal additional effort. (3) Unlike most video generation models, AnimateDiff supports frame-wise luminance control, which naturally aligns with the requirements of animated QR code generation.
In the subsequent sections, we elaborate on the training process of HL.G-ControlNet and the inference process of ProST for animated QR code generation.
3.3 Training HLG-ControlNet
As previously mentioned, although our framework generates animated QR codes in video sequences, we leverage image-based ControlNet to achieve precise luminance control. As illustrated in the red box of Fig. 2, we train the ControlNet following the standard process [43] on a dataset of images paired with their corresponding Hierarchical Luminance Guidance (HLG) maps. The network is trained end-to-end, with HLG maps provided as conditional inputs. We adopt the same loss functions and optimization strategy as ControlNet ensure reproducibility while focusing learning HI. HI.G-guided control signals.
The HLG map is defined as three-channel representation, where each channel corresponds to distinct scale. Each pixel in the HLG map assumes one of three values: -1 for dark regions, for bright regions, and O for regions with unrestricted luminance.
The subsequent section details the process of extracting the HLG map from an input image, as further illustrated in the green box of Figure 2.
Multi-scale Luminance Extraction. For input image I Rmmxma we partition it into m² non- overlapping a x a patches P {Pk im² via grid decomposition. Each patch analyzed using three Multi-scale Central Masks (M, R _ defined with central radii G, [a/2"-1]:
1
if (z,w) Center(c, x cr),
M, (z. v)
(1)
0 otherwise.
The hierarchical luminance values are calculated through:
Σ
Ps (x( M, (I,
(2)
(2,
where 24 is the pixel coordinate space within patch pk and r {1, 2,3) denotes three scales.

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Hierachical Luminance Guidance. Given the average luminance values across different scales, we quantize them to align with the distribution of QR code images. We define ternary quantization function Q [0, (-1,0, } as follows:
(Dark)
if f < Tb: if f > Tas otherwise,
2(m) =
(Bright)
(3)
(Unconstrained)
where Ta 0.7 and тh 0.3 decoder-calibrated thresholds derived from sensitivity analysis. For each patch Ps: the average luminance 1% is mapped to the temnary luminance vector 7 = [2(u)), 2(x)), 2(u2)] according to Equation 2 3.
To dynamically activate scale-specific constraints, we introduce activation vector 3g E B. Here, explicitly defined as the ordered basis set:
B A (bi,..., bg) (00,0,), 1), basis b, (for i = 1, ....... 8) controls the activation of the three scales, with O indicating dcactivation and I indicating activation, thereby enumerating all possible activation patterns. Each value in b corresponds to one scale: activating a scale enables its corresponding blocks to contribute to the content control strength. For instance, (1, O, 0) modifies only coarse-scale blocks, resulting in weaker control, whereas (1, I, 1) involves all scales and achieves maximal control strength. The definition of B and b, facilitates the explanation of how the HLG map updated by modifying the activation vector during inference, as detailed in the following section.
1); (0, 1), (1, 0), (0, 1) 01, (1, 1, 0), (1,1, Each
The final HIG map H is constructed by combining multi-scale constraints through channel-wise concatenation (denoted as θ):
H + h M, (h], H unpatchify((HH
4
During training, we uniformly explore all patterns by sampling 3g such that P(3, b;) for all i {I....,8}, 8). This ensures that the HLG-ControlNet becomes robust spatially dynamic control strengths.
3.4 Inference with ProST
Our diffusion-basee Progressive Spatiotemporal Control (ProST) framework addresses the long- standing control-strength dilemma through an error-adaptive constraint escalation mechanism. Specifically, ProST dynamically modulates hierarchical constraints in response to error-driven con- trol escalation, operating through three distinct phases: Init HLG, Evolve HLG, and Scannability Enhancement. This phased approach ensures a systematic and adaptive progression in constraint enforcement, significantly enhancing the framework's 's robustness and precision in spatiotemporal control tasks. The complete algorithm is presented in Algorithm 1.
Notation. Let k (l...., m2) index the QR modules, where m² represents the total number of modules in the QR code. Let {2,, } denote the latent states at evenly spaced timesteps, where T₁ -A, and A, = T/S. At each stage 8, conditional denoising is performed as:
T DDIM (r., HH" - Ts+1);
(5)
where Hd is the adaptive HI.G map, dynamically updated based on intermediate decoding results.
Given an animated QR sequence V consisting of frames, we partition it into two disjoint subsets: the keyframe set K. {1, N) and the non-keyframe set T (1, .V \.. The keyframes K. are uniformly sampled and are required to satisfy scannability constraints, while the non-keyframes T primarily focus on maintaining temporal coherence without enforcing scannability.
Init HLG. Given QR code q and random noise zr, this module generate initialized HLG map H First, the initial video sequence [ is generated as:
V' Dews (DDIM (2,, H!ll T + 0))
(6)
5

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where H denotes null control, N represents the number of video frames (typically set to 16), and Daze is the VAE Decoder [13].
Next, we align the QR modules with the video content through the following operations:
[Reshuffle(4e.)) q), Interplel--4tt ar), ifLE:
if K;;
d/ = -VV'V''
where: Reshuffle(:;)) is derived from QArt [4] (details provided in the Appendix); Interp(-) performs nearest-neighbor interpolation between two keyframes based on temporal distance; t max I | < t} and ++ min(i E IC > t} denote the nearest preceding and succeeding keyframes relative to frame t; a2 - E (0, 1) represents the temporal interpolation ratio; of E Rm is the reshuffled binary sequence with [b] (-1,
Subsequently, we construct the HLG map for each frame in parallel, where the temporal subscript omitted for simplicity. According to Eq. 4, the HLG map H at stage is formulated as:
( Dl''T) [k] M, gg()))
172
H' = f(d', (.6 ) unpatchify
f(
pm²
(8)
At the initial stage O, we set ((0) by as the initialized activation state.
Evolve HLG. At stage >> 0, given the current estimate 2r, from Eq.5 and the reshuffled QR code o' the module updates the HLG map from H to H=+1 As defined in Eq.8, this is essentially achieved by updating the activation vector set {.8(²) J==1 To achieve error-driven control updates, we first analyze the етrоr distribution. Specifically, the current estimate is decoded as q" arg (Dvae(22 (2r,) )) where Dar denotes the simulated QR decoder [1] (details provided in the Appendix). The module-wise error mask is then computed as:
D 1[0 + [(]],
1sksm'
where [[| is the indicator function, returning if the condition true and otherwise.
Next, we define the evolve function follows:
B(0+1)
(22)) ( otherwise,
if E
(10)
where ó B + (1,..., is a bijective index mapping satisfying <(b:) = i. After this, the updated HLG map Hs+1 is computed following Eq. 8. This update rule ensures that for erroneous modules, the control strength is increased, while for correct modules, the control strength is decreased, thereby achieving balanced trade-off between scannability and aesthetic quality.
Scannability Enhancement. After the final denoising step, the latent code refined using the SELR (Scannability Enhancement via Latent Refinement) module [34]. This module applies gradient- based iterative refinement as follows:
L
on
20 17
V Lwaan (d' (vnn Σ
(11)
1/ v Lucan represents the iterative update with learning rate Lyaan is the scannability loss function (detailed in the Appendix). This refinement process ensures that the final latent code 2ū is optimized for scannability while preserving the visual quality of the generated QR code.
where: L. is the number of SELR iterations, ((+1) =
2D
Experiment

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Experimental Setup and Configura- tion. Our implementation is based on the PyTorch framework and runs on an NVIDIA GeForee RTX 4090 GPU. We generate QR codes of ver- sion 5, corresponding to 37 37 mod- ules (i.e., m 37). For training the HLG--ootrolNet, we adopt *Stable Diffusion v1.5* the backbone and utilize a dataset comprising 60,000 high-resolution images, each prepro- cessed to resolution of 512 512 pixels. During inference, we set the ControlNet control strength to 0.9, the number of frames in AnimateDiff to 16, and the motion scale to 1.0. By default, we define the keyframe set as I. {1, 16] and set the learning rate to r7 = 0.001.
Algorithm 1 Progressive Spatiotemporal Control (ProST)
Require: Initial noise 27, target QR q, stages S
Ensure: Scannable V4
1: Initialize: V vae (DDIM 2T, H T 0))
2: Let g' <- g(V'
3: Let Bg -- (0,0,1) 1). Vk E (1, (1,
4: Let H + f(g', (.0 1 1km1)
mm
5: Let 2To + 2T
6: for to do
7: Denoise: 2x, f- (DDMI 2 37--11
H 1--11 TT-1 - -T,)
8: Decode: q² +- (-Dar (Zr.)
9:
for K to m² do
10:
oz + % then
11:
Dg t minn(6(06 +)1188)
else
13:
gg + b (mxx(
end
15: end for
For comparative evaluation, we con- struct dataset of 500 uniquely styl- ized QR images, each with a resolu- tion of 1024 1024 pixels, encom- passing diverse visual content and artistic styles. To assess scanning robustness, all generated results are displayed a 27-inch, 144Hz IPS monitor. The scannability of dynamic QR codes is tested under real-world conditions by playing the 16-frame animation the screen and performing actual scanning, consistent with their intended practical usage.
Update HLG: H + f(dd, :( f(
pm?
17: end for
Enhance Scannability: 20 SELR(20)
19: Output: Var +- Drae (20
As mentioned earlier, proposed AnimateQR can be casily extended to generate static aesthetic QR code images. This is achieved by disabling AnimateDiff and setting K {1}. We refer to this static variant as AnimateQR-s.
4.1 Qualitative Comparison
Animated QR Code Quality. Due to the absence of dedicated methods for animated QR code generation, extend two representative static image-to- approaches, ArtCoder [31] and Glad- Coder [39], to the video domain via haaeeby--ramm "GladCoder-d",, ensuring fair comparison under identical conditions. As shown in Fig. 3, these baselines exhibit noticeable temporal flickering due to the lack of inter-frame modeling, whereas our method generates animations with significantly improved temporal coherence and visual continuity.
processing. denoted as "AntCoder-d" and
Static QR Code Quality. For fair comparison, we evaluate our static variant, AnimateQR-s. -s against state-of-the-art static QR code generation methods. The comparison includes image-to-QR methods (AnCoddrr [31], GladCoder [39]) and text-to-QR methods (QRBTF [22], Tex12QR [34]). To accommodate varying input requirements, we use unified prompt-image pairs as dual conditioning inputs (details are in the Appendix). As Figure 4 shows, ArtCoder, QRBTF, and GladCoder suffer from color-bloc artifacts, while Text2QR improves aesthetics but exhibits unnatural textures and color shifts. In contrast, AnimateQR-s - delivers visually coherent QR codes with seamless module integration, thanks to our adaptive control strategy that spatially modulates hierarchical constraints based on reconstruction errors.
4.2 Quantitative Comparison
Scanning Robustness. In this study, we evaluate the scanning robustness of our QR codes in comparison with two methods: Text2QR [34] and QRBTF [22]. generate set of 50 aesthetically optimized QR images for each method, all at a resolution of 1024 x 1024 pixels. These images are displayed on a high-definition monitor three standard sizes: em? em? and cm? During controlled testing, smartphones are held at a fixed distance of 40 em, and each QR code is scanned
7

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Table I: Average scanning success rates (%) are assessed across various phone applica- tions, considering different sizes (cm²) and angles ()). "Scanner" denotes the native scanner of system. We compare our method with QRBTF [22] and Tex12QR [34] under same condition.
Table 2: Comparison of animated QR code generation with best results in bold.
Methods
Q-Bencht
SimpleVQAT
Speed
ArtCoder-d GladCoder-d
0.2954
3.0254
0.031
0.2453
2.6901
0.016
AnimateQR
0.6217
3.5872
0.613
Success Rate (%)
Table 3: Comparison of static QR code generation
Decoders
(3cm)²
(5cm)²
(7cm)²
Methods
Q-Aligat
LIQE
AesBench
45 90
45
90
45
90
ArtCoder
0.6003
2.6363
0.4396
QRBTF [22]
GladCoder
0.6559
3.2529
0.7729
Scanner
100
100
100
100
100
100
QRBTF
0.7877
3.5447
0.7822
TikTok
96
96
78
83
56
72
Text2QR
0.7788
3.5034
0.7610
WeChat
100
100
100
98
94
100
AnimateQR-s
0.8433
3.8572
0.8832
Tex12QR [34]
AnimatoQR
Compared Method
Scanner
TikTok
100
100
100
100
100
100
100
100
96
100
100
100
Percenlage (%)
84.8
91.5
75.9
70.7
WeChat
100
100
96
100
94
100
AnimateQR
24.1
29.3
15.2
Scanner
100 100
100
100
100
100
8.5
TikTok
100 100
100
100
96
100
AnCodee
GladCoder
QRBTF
Tex12Q
WeChat
100 100
100 100
96
96
Figure 5: Statistical results of user study.
for 3 seconds from various angles. Scanning success rates, averaged over 20 trials, are reported in
Table 1. Our method consistently achieves success rate above 96%, outperforming the baselines. Notably, QR codes failing within the 3-second window were often successfully scanned with extended exposure, demonstrating the practical reliability of our generated codes.
IQA/ VQA Metrics and Inference Speed. To assess static QR code quality, we adopt Q-Align [38]
(0 - 1), LIQE [44] (0 5), AesBench [12] (0 1), and AesExpert [11, 33] (0 1) as aesthetic evaluation metrics. For animated QR codes, we employ Q-Bench [37] (0 1) and SimpleVQA [2] (0
5). In addition, we report the average inference speed (samples per second) for animated QR code generation. All metrics and runtime statistics are averaged over 100 generated samples per method and summarized in Table - 3. Our method consistently achieves the best performance across both static and animated QR code evaluations.
User Study. To further assess the practical effectiveness of our method, we conduct user study as a supplementary subjective evaluation. The user study consisting of 30 participants evaluate
AntCoder-d
GladCoder-d
AnimateQR
ArtCoder-d
GladCoder-d
AnimateQR
O
O
Figure 3: Visual comparison of animated QR code generation methods. Best viewed with Acrobat Reader. Click the images to play the animation clips and zoom in for finer details.
8

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Input
ArtCoder [31]
QRBTF [22]
GladCoder [39]
Text2QR [34]
AnimateQR
O
Figure 4: Visual comparison of static QR code generation methods.
Table 4: Average QR code scanning success rates
Table 6: Average QR code scanning success rates
(%) across illumination levels ranging from 500
(%) across scanning angles ranging from 09 to
5 LUX.
60%
Methods
500
400
300
200
100
5
Methods
0
15a
30%
45
60
Text2QR-d
100
100
96
82
64
2
Text2QR-d
100
100
100
100
98
QRBTF-d
98
96
90
62
48
0
QRBTF-d
98
100
98
100
96
AnimateQR
100
100
98
88
66
8
AnimateQR
100
100
100
100
98
Table 5: Average QR code scanning success rates (%) across motion blur levels ranging from 0.1
Table 7: Comparison of AnimateQR variants.
AnimateQR uses SDXL + AnimateDiff-XII AnimateQR-LCMM uses AnimateDiff-LCM..
to m/s.
Methods
0.1
0.5
1.0
1.5
2.0
Methods
Q-Bencht
SimpleVQAt
Text2QR-d
100
100
96
90
88
AnimateQR
0.6217
3.5872
QRBTF-d
98
98
92
82
70
AnimateQR-X 0.6926
3.8216
AnimateQR
100
100
96
92
88
AnimateQR-L.CM 0.6138
3.5921
200 generated QR codes (50 for each methods) generated by different methods (the approval from Institutional Review Board is obtained). Participants asked to rank the results in the aspect of aesthetic quality. The percentages represent how many users prefer the results of a method over others. The results are illustrated in Figure 5, with detailed experimental settings provided in the Appendix. The results indicate clear user preference for our method over existing baselines.
Real-World World Robustness. To rigorously evaluate the robustness of our method in real-world see- narios, we conduct three types of tests: varying illumination, motion blur, and scanning angles. As benchmarks, extend Text2QR and QRBTF to dynamic settings via per-frame generation, denoted as "Text2QR-d" and "QRBTF-d". We perform side-by-side comparisons on 50 QR code sequences (each 5 cm 5 em), ensuring a fair and comprehensive evaluation. We conduct controlled experiments: ambient illumination is varied from near 0 to 500LUX across six levels with the phone fixed at 0%, motion blur is simulated along a 3m path speeds of 0. 1.12sm/s under 500LUX, and scanning angles are tested from (8 to 60 with stationary phones under 500LUX. Each method scans the same QR codes 10 times per condition, and average success rates are recorded. The results, summarized in Table 4-6, show that AnimateQR consistently outperforms Text2QR-A -d and QRBTF-d under realistic conditions, including low light, motion blur, and sta-standard scanning angles, achieving superior stability and decoding reliability in challenging scenarios.
Real-
Model Generality. We evaluate the generality and scalability of our framework across different model variants. As shown in Table 7, AniimatemR-XX XL (SDXL AnimateDiff- -i)) shows substantial improvements over the SD-v1.5 variant, while AnimateQR-LCMM (with the distilled Diff ICM) achieves comparable performance. These results demonstrate our framework's 's extensibility to newer architectures and generalizability to distilled models. Moreover, our framework supports style generality by integrating LoRA-traines diffusion models to modify output styles.

---

Table 8: HL Ablation Comparison. Details are in Section 4.3.
Table 9: ProST Ablation. We compare ProST (adaptive 5k) with static baselines using fixed Be by (light control) and 8x by (strong control). ProST achieves better balance between visual quality and scannability, reflected by scanning error rates.
QR-C.Net
HLG-C.Net*
HLG-C.Net
Condition
B3 = b2
B bg
adaptive Br
O
O
D
O
O
Generated
Error Rate:
Error Rate: 2.63%
Error Rate: 11.3%
72.3%
4.3 Ablation Study
HI.G-ControlNet. While prior methods such as Tex12QR [34], Face2QR [5], and GladCoder [39] adopt QRMonster [6] as the ControlNet conditioned on QR code images, we propose HLG- ControlNet, which leverages hierarchical luminance guidance for conditioning. To enhance ro- bustness during training, we introduce random sampling of the modulation vector 3r to simulate varying control strengths. We conduct ablation studies by replacing HLG--CntrooNet (referred to as HLG-C.Net) in AnimateQR with: (1) QRMonster, denoted as QR-C.Net, which serves as the standard ControlNet conditioned on QR code images, and (2) HLG-C.Net", variant trained with fixed modulation vector 3g by. thereby disabling adaptive control. As shown in Table 8, QRMonster often introduces blocky artifacts. HL.G-..Net benefits from hierarchical luminance guidance, yielding more coherent outputs. Our full model achieves the best visual quality, validating the effectiveness of both HI.G-based conditioning and adaptive modulation.
Progressive Spatiotemporal Control (ProST). While previous methods such Text2QR [34], Face2QR [5], and GladCoder [39] employ a fixed control signal throughout inference, we introduce ProST, strategy that adaptively updates the HLG map during inference. To assess its effectiveness, we compare ProST against static control baselines using fixed activation vectors: 3x b2 (0, 0, 1) for light control and 3g- bg (I, 1, 1) for strong control. As shown in Table 9, static light control yields high visual fidelity but poor scannability, whereas strong control improves scannability at the cost of degraded aesthetics. contrast, our ProST strategy with adaptive 3 achieves a superior trade-off, delivering outputs with both robust scannability and high visual quality.
5 Conclusion
We present AnimateQR, a generative framework for animated QR code synthesis. Leveraging hierar- chical luminance guidance and progressive spatiotemporal control, AnimateQR achieves strong balance between visual aesthetics and scannability. Extensive experiments confirm its superiority over prior methods. Beyond QR codes, our adaptive control strategy holds promise for other scannable me- dia, such as animated AR markers, and inspires future work on constraint-aware generative modeling.
Limitations. Despite its strengths, AnimateQR has some limitations. First, generating high- resolution, visually complex animations can be computationally demanding. Second, although designed for broad compatibility, small fraction of legacy less-common scanners may still encounter decoding issues.
Broader Impact. By elevating the visual appeal and personal relevance of QR codes, our work re-imagines them expressive, aesthetic artifacss-unlckking new applications in entertainment, social media, marketing, and personal memorabilia.
Acknowledgment
The work supported by the National Natural Science Foundation of China under Grant 62301310.
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Reviews:
[{"review_id": "2KrA958TdA", "reviewer": "Reviewer_LKx1", "summary": "This paper presents AnimateQR, a novel framework that synthesizes animated QR codes which are both aesthetically pleasing and robustly scannable. The authors address the long-standing trade-off between visual quality and decoding reliability by jointly optimizing animation consistency, visual fidelity to the target image, and robustness to QR decoding constraints. AnimateQR introduces a hybrid optimization framework combining deep generative priors and a differentiable decoder, enabling stylized animation with temporal coherence. Experiments demonstrate that AnimateQR achieves high-quality stylized QR animations while maintaining high scanning success rates, outperforming existing artistic QR approaches in both subjective quality and usability.", "questions": "How does AnimateQR perform under real-world scanning conditions with motion blur, varying screen brightness, or camera shake—particularly for longer or more complex animations? Have you evaluated its robustness in such unconstrained environments, and how might the method be adapted to handle them?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper demonstrates both quantitative robustness (e.g., high scanning accuracy across devices) and qualitative improvements (e.g., user preference studies).\n\n2. The authors release a benchmark dataset and code, which enhances reproducibility and encourages follow-up research in a novel domain.\n\nWeaknesses:\n\n1. The paper does not deeply explore when or why AnimateQR fails (e.g., under low lighting, extreme motion, or unconventional scanning angles), which would be valuable for deployment.\n\n2. While the paper compares against traditional artistic QR methods, it would be useful to include comparisons with more recent diffusion-based stylization models or dynamic code embedding methods.", "was_revised": false, "final_justification": "", "rebuttal": "### **Response to Weakness #1 & Question #1**:\nWe sincerely thank the reviewer for the insightful comments on AnimateQR’s robustness in real-world scenarios. Here we provide the detailed discussion, which will be included in revision.\n\nTo thoroughly assess robustness, we performed three types of tests: varying illumination, motion blur, and scanning angles. All tests involved side-by-side comparisons of AnimateQR, Text2QR, and QRBTF on **50 QR code sequences** (each sized $5\\,\\text{cm} \\times 5\\,\\text{cm}$), ensuring fair and comprehensive evaluation. All the numbers in the tables represent the scanning success rates.\n\n1. **Varying Illumination.** \nUsing a calibrated LUX meter, we varied ambient illumination from near 0 to 500 LUX across six levels. The phone was fixed at a 0° angle to avoid motion or angular interference. Each method (AnimateQR, Text2QR, QRBTF) scanned the same set of animated QR codes 10 times per lighting level, and we recorded the average success rate to evaluate robustness under different lighting conditions.\n\n    | Method      | 500 LUX | 400 LUX | 300 LUX | 200 LUX | 100 LUX | 5 LUX |\n    | :---------- | :------ | :------ | :------ | :------ | :------ | :---- |\n    | AnimateQR   | 100%    | 100%    | 98%     | 88%     | 66%     | 8%    |\n    | Text2QR-*d* | 100%    | 100%    | 96%     | 82%     | 64%     | 2%    |\n    | QRBTF-*d*   | 98%     | 96%     | 90%     | 62%     | 48%     | 0%    |\n\n1. **Motion Blur.**\nTo simulate realistic handheld scanning, we used the phone’s accelerometer and gyroscope to test and control speed (0.1–2 m/s) and motion (≤2 m/s²) along a fixed 3m path. During each test, the phone remained directly facing the screen (0° angle) under controlled lighting at 500 LUX. Each speed was tested separately for AnimateQR, Text2QR, and QRBTF, with each method scanning the QR codes 500 times (10 times per sample). We recorded and averaged the success rates to assess robustness against motion blur.\n\n    | Method      | 0.1 m/s | 0.5 m/s | 1 m/s | 1.5 m/s | 2 m/s |\n    | :---------- | :------ | :------ | :---- | :------ | :---- |\n    | AnimateQR   | 100%    | 100%    | 96%   | 92%     | 88%   |\n    | Text2QR-*d* | 100%    | 100%    | 96%   | 90%     | 88%   |\n    | QRBTF-*d*   | 98%     | 98%     | 92%   | 82%     | 70%   |\n\n1. **Scanning Angles.**\nTo assess performance under non-standard angles, we tested five angles from 0° to 60°, with fixed lighting at 500 LUX and the phone held stationary. Each method (AnimateQR, Text2QR, QRBTF) performed 10 scans per angle. We calculated the average of success rates to evaluate robustness against angular deviation.\n\n    | Method      | 0°   | 15°  | 30°  | 45°  | 60°  |\n    | :---------- | :--- | :--- | :--- | :--- | :--- |\n    | AnimateQR   | 100% | 100% | 100% | 100% | 98%  |\n    | Text2QR-*d* | 100% | 100% | 100% | 100% | 98%  |\n    | QRBTF-*d*   | 98%  | 100% | 98%  | 100% | 96%  |\n\n\nOur experiments show that AnimateQR consistently outperforms Text2QR and QRBTF across realistic scenarios, including low light, motion blur, and non-standard angles. Under ≤100 LUX, traditional methods, especially QRBTF, suffered decoding rates below 60%, while AnimateQR maintained significantly higher success. At ≥300 LUX, all methods performed well, but AnimateQR still led slightly.\n\nWhen scanning speed exceeded 1 m/s, Text2QR and QRBTF showed clear performance drops, with QRBTF falling to 70% at 2 m/s. AnimateQR, by contrast, remained within the 80–90% range, demonstrating robustness from its design.\n\nAcross scanning angles up to 60°, AnimateQR maintained superior success rates with minimal degradation, reflecting strong geometric tolerance to unconventional scanning angles.\n\nOverall, AnimateQR achieves greater stability and decoding reliability under challenging conditions. We will include these results in the revised manuscript to highlight its practical robustness.\n\n---\n\n### **Response to Weakness #2**:\nWe sincerely appreciate the reviewer's suggestion regarding comparisons with state-of-the-art methods. In fact, our evaluation already includes comprehensive comparisons with the latest open-source artistic QR code methods:  \n1) **QRBTF** (online project, diffusion-based)  \n2) **Text2QR** (CVPR 2024, diffusion-based)  \n3) **GladCoder** (IJCAI 2024, diffusion-based)  \n\nTo address the dynamic code embedding aspect, we note that no open-source implementations of dynamic QR code generation are currently available. Therefore, we extended these static methods to dynamic versions (denoted as *MethodName-d*) through per-frame generation, ensuring fair comparison under identical conditions (see **Figure 3** and **Table 2**). \nWe will further emphasize this experimental design in the revised manuscript to improve clarity."}, {"review_id": "5TMmGu7yZ7", "reviewer": "Reviewer_i59R", "summary": "This study proposes AnimateQR to generate QR-decodable animated contents. AnimateQR trains a ControlNet of Stable v1.5. on HLG datasets, and combine AnimateDiff and the proposed ProST sampling technique. The results show that the aesthetic quality is better than the existing approach, while providing the seamless QR codes.", "questions": "Q1. Why the experiments are limited to Stable Diffusion v1.5 + AnimateDiff, which is an outdated checkpoint? There are many open-sourced image and video generative models, and the authors should have shown the generalizability of the proposed approaches across different models.\n\nQ2. Why the domain of generated contents is limited to the anime style? Cannot the model generate photorealistic animations?\n\nQ3. How much important is the ProST technique in terms of quality and QR-decodability? Considering that recent image/video generative models commonly use a diffusion-distillation technique to reduce the number of sampling steps, I'm worrying the incompatibility of the proposed methods with the distilled diffusion models.", "limitations": "The limitation is discussed in the paper.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "S1. As an application study, the animated QR codes are interesting and practical in terms of creative AI.  \nS2. The proposed approach, ControlNet and ProST, makes sense.\n\nW1. I think this paper's main weaknesses lie in limited experiments. Please refer to the section below.   \nW2. The sampling technique is hard to understand and the writing need significant improvements.", "was_revised": true, "final_justification": "First, I sincerely apologize that I couldn't recognize the rebuttal period because my affiliation was changed and the email address became invalid. I read the authors' responses and they address my major concerns. Thus, I increase my score to \"borderline accept\", considering that the authors showed that the method itself is model-agnostic unless the model is one-step generator.\n\nI still think that the proposed method requires more qualitative results on various styles, because the fact that the compared models just focused on anime-styles cannot be a reasonable reason to accept the limited experiments. In addition, while many recent generative models focus on achieving one-step generation, this approach might not be optimal to one-step generator (but I agree that it would a worth exploration for future research too).", "rebuttal": "### **Response to Question #1**:\n\nWe sincerely appreciate the reviewer's insightful question regarding model generality. Our response is twofold:  \n  \n1. **Fair Comparison**: The choice of Stable Diffusion v1.5 (SD-v1.5) + AnimateDiff-v2 as the primary setup aligns with the implementations of all compared methods (QRBTF, Text2QR, GladCoder). This ensures a fair comparison to validate our method's superiority under identical conditions.\n2. **Methodological Generality**: Importantly, AnimateQR is *not* architecture-specific——it is compatible with **any Diffusion+AnimateDiff combination**.\nTo demonstrate extensibility, we conducted additional experiments with Stable Diffusion XL (SDXL) + AnimateDiff-XL, showing significant improvements in both quality and metrics. AnimateQR-XL achieves +7.1% Q-Bench and +6.5% SimpleVQA over SD-v1.5 variant (see table below).\n\n    | Method           | Q-Bench↑   | SimpleVQA↑ |\n    | ---------------- | ---------- | ---------- |\n    | AnimateQR        | 0.6217     | 3.5872     |\n    | **AnimateQR-XL** | **0.6926** | **3.8216** |\n\nThe results validate our approach's extensibility to newer architectures. We will include them in the final paper (Section 4) with expanded discussion on cross-model compatibility. \n\n---\n\n### **Response to Question #2**:\nWe appreciate the reviewer’s insightful question regarding the style generality of our model. The anime-style outputs in our experiments are not a deliberate design choice, but a natural consequence of using the **base Stable Diffusion model** (without additional style-control plugins like LoRA or IP-Adapter) for fair comparison with existing methods.\n\n1. **Baseline Fairness & Focus of This Work**  \n    As shown in Table 2, all compared methods (e.g., Text2QR, GladCoder) adopt the same base model for QR code generation, and thus their outputs similarly exhibit anime-style aesthetics. Introducing photorealism-specific modules would create an unfair advantage in evaluation, as our core contribution focuses on QR pattern alignment, not style control.\n\n2. **Scalability & Flexibility in Practice**  \n   While the current experiments focus on anime-style outputs (generated by the base model without plugins), our framework is **model-agnostic**. As aforementioned, integrating photorealism-controlling tools (e.g., SDXL+LoRA) is straightforward. Photorealistic results via LoRA will be provided in revision.\n\nWe thank the reviewer for raising this important point and will clarify the model’s extensibility in the revised manuscript.\n\n---\n\n### **Response to Question #3**:\nWe sincerely appreciate the reviewer’s insightful question regarding ProST’s compatibility with distilled diffusion models. Here, we clarify three key aspects:  \n\n1. **Theoretical Adaptability**:  \n   ProST is designed to *decouple the denoising process into stages*, updating the HLG map (control signal) at each stage boundary. *This architecture ensures compatibility with any sampling steps > 1*, regardless of whether the backbone is a full diffusion model or a distilled variant (e.g., LCM).  \n\n2. **Empirical Validation with Distilled Models**:\n   We test ProST with *AnimateDiff-LCM* under 4 sampling steps, which denoted as AnimateQR-LCM. We observed that AnimateQR-LCM retained its ability to balance quality and QR scannability. We will include these results in the revised manuscript to demonstrate ProST’s robustness under low-step regimes. \n    | Method        | Q-Bench↑ | SimpleVQA↑ |\n    | ------------- | -------- | ---------- |\n    | AnimateQR     | 0.6217   | 3.5872     |\n    | AnimateQR-LCM | 0.6138   | 3.5921     |\n\n3. **Extension to One-Step Sampling (Edge Case)**:  \n   For extreme cases (e.g., one-step sampling), ProST could be extended by *iteratively regenerating the output with updated control signals*, effectively simulating multi-step optimization.  \n \nWe agree with the reviewer that exploring ProST’s synergy with distillation is valuable, and we will add a dedicated analysis in the revision."}, {"review_id": "SzHdsDU6Re", "reviewer": "Reviewer_J5mD", "summary": "This paper proposes a dynamic QR code generation method that is based on AnimateDiff. To improve the scannability, a HLG control net is trained to progressively control the luminance level of the generated video content.  A  key contribution  of this paper is the proposed progressive spatiotemporal control mechanism developed for denoising process.", "questions": "1. What is  the effect of Reshuffle and Interp operations on the initial video sequence?   It would be nice to show the intermediate results to ease the understanding of the proposed pipeline. \n\n2. Why should the activation vector beta have 3 values?  Is it just for increasing the randomness or necessary for color QR code？\n\n3. why is the animation  small for none-character QR  codes?  please show the initial video sequence to reveal whether it is because of initial video or the subsequence operations.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength: \n\n1. It is inspiring to partition control strength across both spatial and temporal dimensions.\n2.  I test the QR codes presented in this paper and supp., and they guides me to iccv and nips.cc website, which verifies the  scannability of the generated code. \n\nWeakness:\n 1. The organization can be improved. In 3.3 Training HLG-Control Net, there is actually no training procedure of HLG  control net, but the computation process of HLG maps for an image.", "was_revised": true, "final_justification": "AnimateQR is visually appealing. I am positive towards the acceptance of this paper.", "rebuttal": "### **Response to Weakness #1**:\nWe sincerely appreciate the reviewer's insightful observation. In Section 3.3, we indeed focused on explaining the computation of HLG maps (our core contribution) while briefly mentioning the training procedure in Lines 129-130. For clarity, we:  \n1. Followed the standard ControlNet training framework [43], with the key difference being our use of **(image-HLG map)** pairs instead of (image-edge/segmentation) pairs.  \n2. Illustrated the full pipeline in **Figure 2 (red box)**, where the HLG-ControlNet is trained end-to-end by feeding HLG maps as conditional inputs.    \n\nWe will add a dedicated paragraph in Section 3.3 to explicitly describe the (image-HLG) paired training scheme and loss function and optimization details (matching ControlNet's framework). Furthermore, we will clarify that the section's emphasis on HLG computation aims to highlight our methodological novelty, while maintaining reproducibility via standard ControlNet training protocols. \n\nWe believe these changes will better balance innovation disclosure with technical completeness. Thank you for helping us improve this clarity.  \n\n---\n\n### **Response to Question #1**:\nWe appreciate the reviewer's suggestion to visualize the intermediate effects of Reshuffle and Interp operations. While we omitted implementation details in the main paper (as these components serve as preprocessing steps rather than core innovations), we provide a detailed explanation in the **supplementary material**. Below we clarify the key points:\n\n1. **Reshuffle Operation**  \n  Based on the XOR closure property of Reed-Solomon encoding (the core of QR decoding), [5] demonstrated that rearranging black/white blocks to match a target image's luminance distribution preserves scannability.  We adopt this to align the **initial control signal** (QR blocks) with the **generated content**'s luminance, reducing control difficulty.  \n\n1. **Interp Operation**  \n  Applying Reshuffle to every frame would degrade temporal coherence (causing **flickering artifacts**). Therefore, as stated in Lines 171-173, we strategically use Reshuffle only for *keyframes* (scannable) to avoid frame flickering.  For *non-keyframes*, we apply linear interpolation ensures smooth control signal transitions (better visual quality) and preserved scannability (only keyframes need to be scannable).\n\nThis hybrid approach optimally balances scannability requirements with video generation quality. \nWhile we cannot add figures here, we will include visualization of intermediate results in the revised manuscript.\n\n---\n\n### **Response to Question #2**:\nWe appreciate the reviewer's question regarding the design of our activation vector β. The three binary values (0/1) in β are not for randomness but serve a necessary functional purpose in our color QR code framework, as explained below:\n\n1. **Multi-scale Control Mechanism**:  \n   Each value in β corresponds to one of the three scales in our hierarchical control module (Fig. 2 green boxes). When a scale is activated (β=1), its corresponding blocks contribute to the content control strength. For example: *Single-scale activation* (e.g., [1,0,0]): Only coarse-scale blocks are modified, yielding weaker control. *Full activation* ([1,1,1]): All scales participate, achieving maximal control strength.  \n\n1. **Spatial Adaptivity**:  \n   The dynamic update of β across stages enables *adaptive control intensity* in different spatial regions. This balances **scannability** (requiring strong control in error-prone areas) and **aesthetic quality** (weaker control in other regions).  \n\nAs evidenced in Table 5, our adaptive β achieves the optimal balance.\nWe will clarify this rationale in the revised manuscript. Thank you for highlighting this important point!\n\n---\n\n### **Response to Question #3**:\nThe small animation of non-character QR codes simply reflects the weaker motion in their original videos (e.g., static objects/scenes). The generated QR motion scales proportionally to the source video's movement.\nWe can adjust the motion intensity via the motion scale parameter, but the relative motion pattern remains consistent with the original. As suggested, we'll include the initial videos in the revised version for comparison.\n\n---"}, {"review_id": "2Vur3HiUVl", "reviewer": "Reviewer_bZch", "summary": "This paper proposes an animated QR codes generation framework，AnimateQR, which utilizes hierarchical luminance guidance strategy and progressive spatio-temproal control to generate high-quality dynamic QR codes, enabling a good balance between visual quality and scannability. Experimental results validate the effectiveness of the proposed method.", "questions": "1. I'm not an expert in this field, but I don't understand why multi-scale luminance extraction is necessary, and why M3 to M1 simply masks more of the image rather than dividing it into finer-grained grids to extract more detailed luminance.\n2. Since this article generates a continuous 16-frame animation, the quality of the animation will have a direct impact. Therefore, each of the 16 frames should be tested separately, and scanning tests for key frames and non-key frames should also be conducted independently.", "limitations": "The limitations are kindly discussed in the paper.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Extract multi-scale luminance maps from natural images and QR codes to train ControlNet, obtaining HLG-ControlNet for better control over QR code generation.\n2. Combine the animation generation model AnimateDiff to create animated QR codes, giving them higher artistic expression.\n3. An error-driven HLG update strategy was proposed.\nWeaknesses:\n1. It is unclear why multi-scale luminance extraction is performed—the motivation and principles are not well-defined. Specifically, lines 43 to 47 are difficult to correlate or align with the content in Section 3.3 later in the text.\n2. The robustness of QR code scanning appears to have only been tested on static images, with no scanning tests conducted on continuous 16-frame QR codes.\n3. No visualization results for consecutive frames of animated QR codes.\n4. The training details and costs of HLG-ControlNet are missing.", "was_revised": false, "final_justification": "", "rebuttal": "### **Response to Weakness #1 & Question #1:**\nWe sincerely appreciate the reviewer's insightful feedback. Below we clarify the motivation and principles of multi-scale luminance extraction, along with improving the alignment between Sections 1 and 3.3.\n\n**Motivation:**  \nOur multi-scale design stems from empirical observations in prior work: **Overly fine-grained control** (e.g., per-pixel luminance) causes instability and **overly coarse control** (e.g., block-wise) introduces artifacts (Table 4).  \n\nTo balance robustness and visual quality, we propose **spatially adaptive control**. Each module's scale is activated by a learned β (Fig.2 green boxes), dynamically adjusting to local QR readability needs stronger control in low-readability regions (enhancing scannability) and weaker control in high-readability areas (preserving aesthetics). Table 5 qualitatively validates that **fixed-scale controls underperform our adaptive approach**.  \n\n**Principle Clarification:**  \nWe now explicitly map Lines 43-47 to Section 3.3:  \n- **(1) Module-aligned Encoding** → Lines 136-140: Feature extraction per QR module.  \n- **(2) Hierarchical Control** → Lines 141-155: Multi-scale interaction via β-weighted fusion.\n- **(3) Adaptive Constraint** → Lines 156-158: Train with stochastic relaxation to make the model robust to spatially dynamic control strengths.  \n\n\nWe will strengthen the motivation discussion in Section 1, insert cross-references between Sections 1 and 3.3 for better flow and re-polish the description for clarification.\n\n---\n\n### **Response to Weakness #2 & Question #2:**\n\nWe sincerely appreciate the reviewer’s valuable feedback regarding the robustness testing of QR code scanning.  \n\n1. **Dynamic QR Code Scanning Test**:  \n   We clarify that **the scannability of dynamic QR codes was indeed tested under real-world conditions**—by displaying the 16-frame animation on a screen and performing scanning tests. This aligns with the intended use case of dynamic QR codes. We will provide additional details about the experimental setup (e.g., display specifications, scanning distance/angle) in the revised manuscript to enhance reproducibility.  \n\n\n2. **Per-Frame Scannability Analysis**:  \n   We fully agree with the reviewer’s suggestion to evaluate individual frames. Here we supplement a **per-frame scannability test**:  \n \n   | Frame Index      | 1    | 2   | 3   | 4   | 5   | 6   | 7   | 8    | 9   | 10  | 11  | 12  | 13  | 14  | 15  | 16   |\n   | ---------------- | ---- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | --- | --- | --- | --- | --- | ---- |\n   | Keyframe?        | Yes  | No  | No  | No  | No  | No  | No  | Yes  | No  | No  | No  | No  | No  | No  | No  | Yes  |\n   | Scannability (%) | 98.7 | N/A | N/A | N/A | N/A | N/A | N/A | 99.2 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 98.5 |\n\n   Results confirm that **keyframes maintain high scannability (>98%)**, while non-keyframes are intentionally non-scannable as per design. This table will be included in the revised manuscript. Experimental setting follows the Table 1 with size $5cm^2$ and angle $90^o$.\n\n---\n\n### **Response to Weakness #3:**\nWe sincerely appreciate the reviewer’s feedback. In the submitted PDF (viewable with Adobe Acrobat), all dynamic QR codes (Figure1 and Figure3) are clickable for playback. To further improve readability, we will provide visualization of consecutive frames in supplementary material.\nThis update will ensure readers can easily visualize the results without dependency on specific PDF viewers.  \n\n---\n\n### **Response to Weakness #4:**\nWe appreciate the reviewer's attention to the training details. As noted in Lines 128-130, the training protocol of HLG-ControlNet follows the original ControlNet paper for consistency. Since our core contribution lies in the proposed *image-HLG map* data generation pipeline (detailed in Section 3.3), we focused the main text on this novel component.  \n\nTo address the reviewer's concern, we will expand the main text with a brief summary of training hyperparameters (e.g., batch size, epochs) in Section 3.3. Furthermore, we wil add a subsection in the supplement with full training costs (GPU hours, hardware specs) and implementation details.\nWe believe this clarification will better highlight our contribution while providing reproducibility.  \n\n---"}]

Author rebuttal:

Meta-review: This paper introduces AnimateQR, the pioneering generative framework for creating animated QR codes that seamlessly blend aesthetic flexibility with scannability. All reviews are highly positive, specifically noting BA, BA, BA, BA. Reviewer LKx1 emphasized the robust quantitative results, such as high scanning accuracy across various devices, as well as qualitative enhancements evident in user preference studies. Reviewer i59R deemed the animated QR code application both practical and innovative. Reviewer J5mD independently verified the scannability of the generated QR codes. Reviewer bZch acknowledged the novel innovation.
The authors' rebuttal has adeptly addressed the majority of concerns raised by the reviewers. Overall, I recommend the acceptance of this submission. Additionally, I expect that the authors will incorporate the new results and suggested modifications from the rebuttal phase into the final version.
Decision: Accept (poster)