TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control

Summary

Considering the limitations of current GAN-based and Diffusion-based Scene Text Editing (STE) methods, this paper introduces TextCtrl, a diffusion-based method that edits text with prior guidance control. Specifically, TextCtrl incorporates text style disentanglement and text glyph structure representation to control the style and structure of generated scene text images, respectively. During inference, a Glyph-adaptive Mutual Self-attention mechanism is designed to further enhance style consistency and visual quality. Additionally, a new real-world image-pair dataset called ScenePair is crated for fair evaluation. Experiments demonstrate that TextCtrl outperforms previous methods in terms of style fidelity and text accuracy.

Strengths

1. The paper is well written, and the presentation is clear 2. In the field of STE, the proposed method is reasonable in targeting structural integrity and stylistic consistency. By 3 prior guidance control of text style disentanglement, text glyph structure representation and source image reconstruction, TextCtrl can generate edited image with both high style fidelity and high recognition accuracy, which is novel. 3. The paper constructs the first real-world image-pair dataset for STE, which is contributed to the community. 4. The experimental results show that the proposed method surpasses previous methods with large margins in most cases.

Weaknesses

1. Several hyperparameters are included in the TextCtrl. I am not sure is there any experience for users to set up these hyperparameters for different datasets. 2. Some figures could be improved. For example, Figure 3 is somewhat not easy to follow; highlighting the pretrained parts and the trainable parts would make it clearer. Figure 6 appears chaotic, it may be better to choose fewer texts.

Questions

1. Can this method be used on Chinese scenes? 2. The authors reference the way of structure condition insertion in ControlNet to feed style condition into TextCtrl, i.e., zero convolution and add, rather than structure condition. Could the authors explain this modification? BTW, could the authors introduce more experimental settings for ControlNet in Table 3, including inputs? 3. In Fig.3, why using "sifted" for c_texture but "Despairing" for c_spatial?

Rating

7

Confidence

5

Soundness

4

Presentation

3

Contribution

3

Limitations

Yes, the limitations are detailed discussed.

Summary

This paper aims to enhance scene text editing performance using a conditional prior-guidance-control diffusion model. It decomposes text style into background, foreground, font glyph, and color features. A text glyph structure representation improves the correlation between the text prompt and glyph structure. For inference, a glyph-adaptive mutual self-attention mechanism with a parallel sampling process is proposed. To evaluate effectiveness, the ScenePair image-pair dataset is created. Experiments on ScenePair and TamperScene datasets, along with ablation studies, are conducted.

Strengths

1) Text glyph structure representation addresses the weak correlation between text prompts and glyph structures in text-to-image diffusion models. 2) The proposed glyph-adaptive mutual self-attention ensures coherent control during inference, guided by source image reconstruction, which is novel. 3) The ScenePair dataset is valuable for research on STE tasks. 4) Both quantitative and qualitative experimental results demonstrate notable performance improvement across multiple datasets.

Weaknesses

1) For the ablation study of text style disentanglement, why replace the style encoder with ControlNet? 2) Some sentences are too long which impacts the clarity. For example, line 235 to line 239, the sentence “GAN-based methods generally … due to the unstable restoration quality” expands 5 lines, and it will be more clear if it is split into two or more sentences. 3) Some figures and tables are not convenient for reading, such as Fig. 4 & 5, Tab. 4 & 5. They should be swapped so that the main parts are near the corresponding figure or table.

Questions

See the weaknesses

Rating

7

Confidence

4

Soundness

4

Presentation

3

Contribution

4

Limitations

The authors have pointed out the limitations in the Appendix.

Summary

This paper proposes TextCtrl, which is a new method for high-fidelity scene text editing. The authors identify the primary factor hindering previous methods from achieving accurate and faithful scene text editing to be the absence of prior guidance on stylistic elements and textual organization. By leveraging disentangled text style features and robust glyph structure guidance, TextCtrl achieves superior editing performance. A glyph-adaptive mechanism and a new ScenePair dataset for real-world evaluation further solidify TextCtrl's effectiveness.

Strengths

- The paper is well-written. - The experiments are extensive and well-discussed. - The dataset is valuable for the research community.

Weaknesses

- The paper misses details and discussions on key contributions (please see the Questions section below), making it hard to evaluate the significance of the contributions of the work.

Questions

- Sec. 3.2 on text style disentanglement only provides implementation details but doesn’t give the big picture of why all these components working together would successfully learn to disentangle the text style. Since this seems to be one of the main contributions of the paper, I’d recommend to include more discussions on what each component achieves. - Line 189 mentions integration of the K, V of the reconstruction branch and K, V of the main branch is preferred over the replacement that is often done in the literature. Could you provide some insights on why this might be more helpful, and in which scenarios?

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

2

Limitations

The limitations have been thoroughly discussed in the appendix.

Summary

This manuscript proposes a diffusion-based scene text editing method with prior guidance control. It incorporates style-structure guidance into the model to enhance the text style consistency and rendering accuracy. A Glyph-adaptive mutual self-attention mechanism is designed to deconstruct the implicit fine-grained features to improve the generation quality. Besides, a new real-world image-pair dataset is proposed for fair comparisons. The experimental results show that the proposed method achieves the best results among existing methods.

Strengths

**Innovative Approach**: The paper introduces a novel diffusion-based method that leverages prior guidance control for text editing, addressing the limitations of previous GAN-based and diffusion-based methods. **Fine-grained Style Disentanglement**: By constructing a fine-grained text style disentanglement, the method improves text style consistency, which is crucial for maintaining the original style and texture during editing. **Robust Glyph Structure Representation**: The system incorporates a robust representation of text glyph structures, enhancing the accuracy of text rendering. **Comprehensive Evaluation**: The authors have created the ScenePair dataset to evaluate both visual quality and rendering accuracy, providing a more holistic assessment of STE methods.

Weaknesses

**Limitation in Task Scope**: The proposed method, while highly specialized in the editing domain, is limited to the task of text editing and does not encompass text generation capabilities. This contrasts with previous methods that offer a dual functionality of both generation and editing. The inability to generate new text content as well as edit existing text may be considered a significant limitation, restricting the method's applicability in scenarios that require creative text synthesis in addition to editing. **Insufficient Ablation Study**: Although the proposed method decomposes text style processing into four distinct tasks—text color transfer, text font transfer, text removal, and text segmentation—the paper does not present a comprehensive ablation study that systematically evaluates the contribution of each individual task. An ablation study is crucial for understanding the impact of each component on the overall performance. Without it, the discussion lacks depth regarding the significance and interplay of these tasks in achieving the method's objectives. Therefore, further research is needed to dissect the individual contributions and optimize the balance between these tasks for improved performance and efficiency.

Questions

1. Can the proposed method deal with generation task directly, as mentioned in Weaknesses? 2. The ablation study for the four tasks is needed, as mentioned in Weaknesses.

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

Yes

Summary

The paper addresses the challenges of Scene Text Editing (STE) by introducing a diffusion-based STE method, TextCtrl. Traditional GAN-based STE methods struggle with generalization, and existing diffusion-based methods face issues with style deviation. TextCtrl overcomes these limitations through two main components: (1) Style-Structure Guidance: By disentangling fine-grained text styles and robust text glyph structures to improve text style consistency and rendering accuracy. (2) Glyph-adaptive Mutual Self-attention Mechanism: it enhances style consistency and visual quality by reconstructing implicit fine-grained features of the source image. Additionally, the paper introduces ScenePair, the first real-world image-pair dataset designed for STE evaluation.

Strengths

(1) Style-Structure Guidance: By disentangling fine-grained text styles and robust text glyph structures to improve text style consistency and rendering accuracy. (2) Glyph-adaptive Mutual Self-attention Mechanism: it enhances style consistency and visual quality by reconstructing implicit fine-grained features of the source image. (3) This paper propose an image-pair dataset termed ScenePair to enable the comprehensive assessment on real-world images.

Weaknesses

(1) In this paper, the authors mention that ‘their style guidance predominantly originates from the image’s unmasked regions, which can be unreliable in complex scenarios and fail in style consistency.’ In theory, the unmasked region can provide the editing model with more style prior information from the surrounding environment, which is beneficial for style transfer during the editing process. (2) An important characteristic in text editing is to ensure that the edited word is compatible with the features of the surrounding unmasked region of the original image. Similar approaches have already emerged, such as TextDiffuser and AnyText, as mentioned in your paper. Therefore, the setting of editing only cropped images does not guarantee this compatibility. Additionally, the method proposed in the paper involves relatively complex design choices. (3) About the ScenePair benchmark: how do authors ensure that the backgrounds of image pairs are consistent? Or are there some examples where the image-pair’s backgrounds are noticeably mismatched after your filtering rules? (4) About the evaluation: During the evaluation of inpainting-on-full-size-image method DiffSTE, TextDiffuser, and AnyText, do authors replace the unedited region with the origin image when comparing with the cropped-based method.

Questions

Please see weaknesses section.

Rating

5

Confidence

5

Soundness

3

Presentation

3

Contribution

3

Limitations

Please see weaknesses section.

post-rebuttal

After reviewing the feedback and rebuttal, I find that the concerns have been addressed. I will maintain my rating.

The rebuttal has been read

After reviewing the feedback and rebuttal, I would like to maintain my rating.