MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis
MetaDesigner
Yes
No ethics review needed.
**Glyph and Texture** Will separating Glyph and Texture limit the diversity of generated results? Could you provide additional ablation studies to demonstrate the necessity of decoupling Glyph and Texture?

**User Interface** Can you provide more details about the user interface and how users interact with the system? How intuitive is the user experience?

**Failure Case** It would be best to explain the existing artifacts in the current system and suggest potential areas for improvement in future work.
**Innovative Framework** The use of a multi-agent system (Pipeline Designer,
Glyph Designer, Texture Designer, and Q&A Evaluation Agent) and the integration of large model to drive the design process is a novel approach that addresses the challenges of creating high-quality WordArt.

**User-Centric Design** The comprehensive feedback mechanism, which includes insights from multimodal models and user evaluations, ensures that the generated WordArt aligns closely with user preferences and expectations.

**Multilingual Capabilitie** The system demonstrates robust performance across English, Chinese, Japanese, and Korean, making it versatile and suitable for a wide range of applications.

**Empirical Validation** The paper provides detailed comparisons with state-of-the-art methods, showing that MetaDesigner outperforms existing techniques in terms of WordArt synthesis success, quality, diversity, and creativity.
This paper introduces a novel framework for artistic typography synthesis that leverages Large Language Models (LLMs) to drive an interactive and user-centered design process. The system is built on a multi-agent architecture, including Pipeline, Glyph, and Texture agents, which enable the generation of highly customizable WordArt. 
The authors introduce a comprehensive feedback mechanism that integrates insights from multimodal models and user evaluations to iteratively refine hyperparameters, ensuring that outputs align with specific stylistic and thematic preferences.
**Complexity and Usability** The multi-agent system and the iterative optimization process may be complex for non-technical users, potentially limiting the system's accessibility.

**Computational Resources** The paper does not provide a detailed analysis of the computational resources required to run MetaDesigner, which is important for practical deployment.
Yes
No ethics review needed.
I'd like to pose questions that can address the weaknesses discussed.
1.  **More Detailed Examples of the Full Pipeline**: While Fig. 10 presents the final results, it would be beneficial to show the output at each step of the system, such as the glyph images generated by the “Glyph Designer Agent.” Subfigures (like the picture with dark green tones in the lower left corner) may be good choices for illustrating these intermediate steps.

2.  **Details on the Construction of the Style LoRAs Library in the “Texture Designer”**: Additional information is expected regarding the number of trainable parameters, the volume of collected data, and a comparison of data size with previous WordArt synthesis works, among other relevant details.

3. **Information about the inference efficiency**: A comparison of image generation time and computational resources required, relative to other models

4. **Impact of Replacing with GPT4-V**: a) In both the “Texture Designer” and “Parameter Optimization” stages, image input is utilized. Would replacing the previous GPT-4 with GPT4-V be a more effective choice? Quantitative and sample-based analyses? b) What differences would arise if using GPT4-V instead of LLava in the “Feedback” stage?

5. **Potential Common Problems in Generated Images**: a) Although the images in Fig. 5, Fig. 8, and Fig. 9 may possess artistic qualities, the text color often resembles the background, making it less distinguishable compared to results from other models.  b) In the lower-left of Fig. 8, style information, such as “Renaissance,” is generated as part of the text—what causes this, and is this phenomenon common?
1. The WordArt examples generated by the model in this paper demonstrate higher visual quality, more accurate text representation, and greater consistency in overall style compared to previous methods.

2. The system proposed in this paper can generate WordArt in diverse styles, closely resembling designs created by human designers, owing to the use of a style LoRA library. Additionally, it demonstrates the effect of merging LoRAs with varying weights.

3. The paper utilizes advanced prompt-LM techniques, such as Chain-of-Thought, Tree-of-Thought, and Feedback Refinement, to enhance the accuracy of sub-prompts for each agent, resulting in generated content that better aligns with user preferences.
This paper introduces a multi-agent system capable of generating WordArt based on user instructions, with flexibility in style and texture. The system primarily consists of a Pipeline Designer, a Glyph Designer, and a Texture Designer, each responsible for overall process design, generating font images, and applying background and text textures, respectively. A feedback mechanism is integrated into the system, where prompt refinement is achieved through interactions between GPT and LLava or users, improving prompt to produce higher-quality images that better align with user expectations.
1. **Lack of novelty**: The improvements in “Accuracy and Creativity” claimed in the paper primarily depend on the construction of the style LoRAs library in “Texture Designer”, which aligns with previous experience. The impact and novelty of other modules are also insufficiently highlighted. The paper adopts a style closer to a technical report.

2. **Insufficient Experimental Design**: For example, (a) the user study includes only 11 participants, and it may benefit from a fair comparison using publicly available VLMs (e.g., GPT4-V); (b) the ablation study only analyzes the ToT module within the “Texture Designer,” without examining the analyses of other modules; (c) the comparison of generated results with other models is limited to 6 samples in Fig. 5, and more samples would be expected.
Yes
No ethics review needed.
1. Should Equation (13) use max instead of argmax?

2. For the hyperparameter optimization in Section 3.4, it seems that the parameters are updated with GPT (as illustrated in Figure 4). What are the detailed prompts used to instruct GPT to output the updated prompts/parameters for each case in Figure 4?

3. For the user study, what does “Text accuracy” mean? Was the input text exposed to the participants during the study? What is the meaning of the percentages in Table 1?
1. Artistic text generation is an important problem, whose solutions can be useful in many practical scenarios.

2. The amount of the contribution is non-trivial since a lot of effort has gone into adapting and connecting different large models together to form a complete, large working system. 

3. The whole system is well motivated and designed.
The paper studies the problem of generating stylistic text (with stylized glyph and texture) from input text prompts.

The core contribution is a multi-agent system that factors text generation into multiple sub-tasks, and addresses them with specialized LLM, LMM and stable diffusion agents, including: 1)  a pipeline designer for generating design programs to instruct other agents; 2) a glyph designer for glyph generation; 3) a texture designer for texture generation;  4) a Q&A evaluator that gathers user and LMM feedback for hyperparameter tuning of other agents to iteratively improve output quality.
1. Some technical details are missing. For example, in “Normal & Traditional Glyph”, the rendering details of normal and traditional glyphs based on $s^{gly}$ are unclear. Also, it is not clear how vector graphics optimization in “Semantic Glyph Transformation” is done. Where do the “target objects” come from and what is the loss function exactly? In Section 3.4, what is the meaning of $L_m$ and $L_u$ ?

2. Some experimental settings are questionable. First, comparison to SDXL is unfair. Traditional text-to-image models like SDXL often require detailed prompts and may not work well with brief instructions (e.g., “Create a stylish word World Peace representing its meaning”) that the proposed system is supposed to handle. The proposed system utilizes GPT for input prompt extension and, thus, it should be compared against a GPT-augmented SDXL where GPT is used to extend the input prompt into a more detailed description relevant for text generation.  Second, in the user study, “Aesthetics & Creativity” should be divided into two independent dimensions, instead of being merged as a single one. Third, the claim “Table 2 presents quantitative results using SSIM and LPIPS metrics, further reaffirming the excellence of our approach in terms of readability and aesthetics.” is inadequate. SSIM and LPIPS mainly measure image similarity and, therefore, are not suitable for evaluating readability. To compute quantitative scores for readability and legibility, a more reasonable way may be to apply a model to detect and recognize text on the generated images and then compare the outputs with the ground truth text. Last, the “quantitative” evaluation of the Q&A evaluation agent is lacking. The results of the proposed method, without this agent, should be added to Table 1 and Table 2. 

3. Some results are not satisfactory. As shown in Figure 5, the outputs by the proposed method, although being visually appealing, suffer from low text legibility and readability: some letters and words are either hard to recognize due to some of their strokes being excessively deformed or stylized (e.g., as leaves), or hard to read because of low color contrast between them and the background.
@inproceedings{
anonymous2024metadesigner,
title={MetaDesigner: Advancing Artistic Typography through {AI}-Driven, User-Centric, and Multilingual WordArt Synthesis},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Mv3GAYJGcW},
note={under review}
}
MetaDesigner revolutionizes artistic typography synthesis by leveraging the strengths of Large Language Models (LLMs) to drive a design paradigm centered around user engagement. At the core of this framework lies a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively enable the creation of customized WordArt, ranging from semantic enhancements to the imposition of complex textures. MetaDesigner incorporates a comprehensive feedback mechanism that harnesses insights from multimodal models and user evaluations to refine and enhance the design process iteratively. Through this feedback loop, the system adeptly tunes hyperparameters to align with user-defined stylistic and thematic preferences, generating WordArt that not only meets but exceeds user expectations of visual appeal and contextual relevance. Empirical validations highlight MetaDesigner's capability to effectively serve diverse WordArt applications, consistently producing aesthetically appealing and context-sensitive results.
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MetaDesigner
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applications to computer vision, audio, language, and other modalities
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MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis
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