Title: AnimateQR: Bridging Aesthetics and Functionality in Dynamic QR Code Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: ControlNet

AnimateDiff

diffusion

QR code
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe limitations are kindly discussed in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-18"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strength: 

1. It is inspiring to partition control strength across both spatial and temporal dimensions.
2.  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. 

Weakness:
 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.

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. 

2. Why should the activation vector beta have 3 values?  Is it just for increasing the randomness or necessary for color QR code？

3. 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
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

comments: 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.

STRENGTHS AND WEAKNESSES:
S1. As an application study, the animated QR codes are interesting and practical in terms of creative AI.  
S2. The proposed approach, ControlNet and ProST, makes sense.

W1. I think this paper's main weaknesses lie in limited experiments. Please refer to the section below.   
W2. The sampling technique is hard to understand and the writing need significant improvements.

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.

Q2. Why the domain of generated contents is limited to the anime style? Cannot the model generate photorealistic animations?

Q3. 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.
TITLE: Official Review
CLARITY: 1
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nHow 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}