MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis

## Abstract

Abstract 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.

## 1 Introduction

Typography, a fusion of linguistic expression and visual design, plays a key role in various domains, including advertising Cheng et al. (, , ); Sun et al. (), education Vungthong et al. (), and tourism Amar et al. (). As a medium for communication, artistic expression, and innovation, typography requires a deep understanding of aesthetics and design principles, posing significant challenges for non-professionals. The allure of typography lies not only in its ability to convey information but also in its capacity to evoke emotions and captivate audiences through imaginative and visually appealing designs.

Generative models have revolutionized typographic design by adapting to diverse aesthetic preferences. However, integrating these models to meet complex typesetting requirements presents notable challenges. (1) The subjective nature of artistic typography, influenced by individual and cultural factors, complicates the development of generative models that resonate widely. (2) The scarcity of comprehensive, annotated resources in artistic typography hinders models’ ability to capture and generate diverse typographic styles. Despite significant advancements in text-to-image synthesis, such as denoising diffusion probabilistic models Ho et al. (); Ramesh et al. (); Song et al. (), they remain insufficient in addressing these specific challenges.

Figure: Figure 1: Overview of MetaDesigner, showcasing the interplay between the Pipeline, Glyph, and Texture agents, orchestrated to tailor WordArt in alignment with user preferences.
Refer to caption: x2.png

To tackle these challenges head-on, we introduce MetaDesigner, a multi-agent system designed to generate artistic typographies aligned with user preferences. MetaDesigner integrates four dedicated intelligent agents: Pipeline Designer, Glyph Designer, Texture Designer, and Q&A Evaluation Agent, each playing a crucial role in generating personalized artworks and ensuring a comprehensive, user-centric design process (see Sec. 3). The Pipeline Designer orchestrates the system, translating user prompts into structured tasks for other agents (see Sec. 3.1). The Glyph Designer creates diverse glyph types, including regular, traditional, and semantic glyphs, customized according to the context of the artwork (see Sec. 3.2). The Texture Designer enhances glyphs with various texture styles using LoRA model matching (see Sec. 3.3) on the provided tree structure. Finally, the Q&A Evaluation Agent refines the output through an iterative process that incorporates feedback (see Sec. 3.4). Overall, MetaDesigner democratizes artistic text creation by focusing on three key aspects.

- •
MetaDesigner introduces a multi-agent system that integrates evaluation and optimization modules to facilitate the discovery and realization of personalized artistic text styles. The system’s hyperparameter selection process ensures the generation of artworks that align with individual preferences. The accessibility and popularity of the model are demonstrated by the over 500,000 visits it has received on.
- •
The system utilizes a hierarchical model tree containing 68 LoRA models, covering various design aspects to ensure rich diversity in the generated output. This advanced glyph design capability significantly expands the scope of artistic expression in typographic design.
- •
MetaDesigner showcases our commitment to supporting the artistic typography research community by developing a carefully curated dataset of artistic texts. The dataset contains over 5,000 multilingual images, spanning English, Chinese, Japanese, and Korean, covering a wide range of artistic styles and cultural elements. This extensive dataset serves as a valuable resource for researchers and practitioners to explore new techniques, evaluate existing methods, and push the boundaries of artistic text generation.

## 2 Related Work

Text-to-Image Synthesis. Denoising diffusion probabilistic models have revolutionized text-to-image synthesis Ho et al. (); Song et al. (); Dhariwal & Nichol (); Nichol & Dhariwal (); Saharia et al. (); Ramesh et al. (); Rombach et al. (); Chang et al. (), enabling interactive image editing Meng et al. (); Gal et al. (); Brooks et al. () and multi-condition controllable synthesis Zhang & Agrawala (); Mou et al. (); Huang et al. (). Methods like ELITE Wei et al. (), UMM-Diffusion Ma et al. (), and InstantBooth Shi et al. () leverage CLIP image encoders to embed visual concepts into text embeddings.

Visual Text Generation. Generating legible text within images is challenging Rombach et al. (). GlyphDraw Ma et al. () and GlyphControl Yang et al. () focus on rendering and aligning characters, while TextDiffuser Chen et al. () introduces character-level segmentation masks. Large-scale language models are crucial for visually accurate text generation Saharia et al. (); Balaji et al. (); Lab (). However, character-blind text encoders struggle with non-Latin scripts Liu et al. (). GlyphDraw addresses Chinese text rendering by fine-tuning the text encoder and using CLIP for glyph embeddings Ma et al. (), while DiffUTE employs a pre-trained image encoder for glyph extraction in image editing Chen et al. ().

WordArt Synthesis. Synthesizing WordArt Tanveer et al. (); Iluz et al. (); Berio et al. (); ; Zhang et al. (); He et al. (, ) requires integrating semantics with artistic and legible text representation. Early attempts  explored substituting letters with semantically similar icons. Recent advancements leverage large generative models, with Word-As-Image Iluz et al. () introducing artistic typography for Latin text and DS-Fusion Tanveer et al. () synthesizing intricate text forms, including hieroglyphics.

MetaDesigner distinguishes itself by integrating specialized agents—Pipeline Designer, Glyph Designer, Texture Designer, and Q&A Evaluation Agent—into an interactive system that iteratively refines WordArt based on user feedback. This adaptive approach addresses a broader spectrum of user preferences and creative demands compared to previous methods.

Figure: Figure 2: MetaDesigner Architectural Overview: This schematic illustrates the structured integration of MetaDesigner’s three core intelligent agents—Pipeline Designer, Glyph Designer, and Texture Designer—each contributing to personalized WordArt creation. The system also incorporates a Q&A Evaluation agent that collaboratively enhances output quality. The diagram highlights the interactive, iterative process through which textual inputs are transformed into visually compelling, user-preference-driven artistic typography.
Refer to caption: x3.png

## 3 MetaDesigner Framework

The MetaDesigner framework is an interactive multi-agent system for synthesizing WordArt that aligns with user preferences. It integrates four specialized intelligent agents: Pipeline Designer, Glyph Designer, Texture Designer, and Q&A Evaluation Agent, each playing a crucial role in generating personalized WordArt.
The WordArt image $\hat{I}$ synthesized by MetaDesigner, denoted by $\Psi$, is mathematically represented as:

$$ $\small\hat{I}=\Psi(s^{\text{user}},\phi,\mathcal{P},\mathcal{M})$ (1) $$

where $s^{\text{user}}$ signifies a user’s prompt, encapsulating their preferences and input. The term $\phi$ represents the collective functionality of the involved agents: $\phi=\{\phi^{\text{pip}},\phi^{\text{gly}},\phi^{\text{tex}},\phi^{\text{qa}}\}$, corresponding to the Pipeline Designer, Glyph Designer, Texture Designer, and Q&A Evaluation Agent, respectively. $\mathcal{M}$ is the library of models utilized by the Texture Designer, while $\mathcal{P}$ denotes a set of learnable hyperparameters designed to fine-tune the system’s outputs to closely match user preferences through interactive and context-aware learning. $\mathcal{P}=\{\mathcal{P}^{\text{pip}},\mathcal{P}^{\text{gly}},\mathcal{P}^{
\text{tex}},\mathcal{P}^{\text{qa}}\}$, with each subset specifically allocated to its corresponding agent. $\mathcal{I}$ symbolizes the WordArt image crafted in the preceding iteration, highlighting the capacity for iterative learning and adaptation to user preferences:

$$ $\displaystyle\small\hat{I}$ $\displaystyle=\Psi(s^{\text{user}},\phi,\mathcal{P},\mathcal{M})$ (2) $\displaystyle=\phi^{\text{pip}}(s^{\text{user}})\cdot\phi^{\text{tex}}(\phi^{ \text{gly}}(s^{\text{gly}}),s^{\text{tex}},\mathcal{M})\cdot\phi^{\text{qa}}(I _{\text{prev}},\mathcal{P}^{\text{qa}})$ $$

where $\mathbf{S}=\phi^{\text{pip}}(s^{\text{user}})$ generates the extension prompt, further decomposed into $\mathbf{S}=\{s^{\text{gly}},s^{\text{tex}}\}$, which are tailored prompts directing the Glyph and Texture Designers. $\phi^{\text{qa}}$ evaluates and adjusts the parameters based on the feedback from the Q&A Evaluation Agent. The following sections introduce each agent module in detail.

### 3.1 Pipeline Designer Agent

The Pipeline Designer transforms visual tasks into a structured coding style using visual programming techniques, enabling precise coordination among synthesis agents. This approach enhances the system’s accessibility and usability for end-users by generating a series of questions that guide the GPT-4 model through a Chain-of-Thought (CoT) process, which breaks down complex tasks into manageable steps and adapts the workflow based on iterative feedback.

Visual Programming. As demonstrated in previous work and Fig. 2, we leverage GPT-4’s in-context learning ability to generate visual programs from natural language instructions, without the need for fine-tuning. The in-context examples consist of manually written programs that can typically be constructed without an accompanying image. Each program step comprises a module name, input argument names and values, and an output variable name.

Prompt Extension. To tailor the system to user preferences based on image style, application domain, and background, the Pipeline Designer employs a series of questions to guide GPT-4 through a CoT reasoning process. Given a user prompt $s^{\text{user}}$, the Pipeline Designer extends it using the GPT-4 to create an enriched prompt $\mathbf{S}$:

$$ $\small\mathbf{S}=\phi^{\text{pip}}(s^{\text{user}})=\{s^{\text{gly}},s^{\text{ tex}}\}$ (3) $$

where $\phi^{\text{pip}}$ represents the Pipeline Designer function, and $s^{\text{gly}}$ and $s^{\text{tex}}$ are the specific prompts for the Glyph Designer and Texture Designer, respectively.

Feedback Integration. The Pipeline Designer incorporates feedback to refine the workflow and align the synthesis process with user preferences. This feedback loop is formalized as:

$$ $\small\mathcal{P}^{\text{pip}}_{\text{new}}=\mathcal{F}(G|s^{\text{update}})$ (4) $$

where $\mathcal{F}$ is the feedback function, $G$ represents the aggregated feedback, and $s^{\text{update}}$ is the update signal for the Pipeline Designer’s hyperparameters $\mathcal{P}^{\text{pip}}$. The overall process of the Pipeline Designer is summarized as:

$$ $\mathbf{S},\mathcal{P}^{\text{pip}}_{\text{new}}=\phi^{\text{pip}}(s^{\text{ user}},\mathcal{P}^{\text{pip}},\mathcal{F}(G|s^{\text{update}}))$ (5) $$

This formulation captures the interplay between user input, prompt extension, and feedback integration. By integrating user preferences, extending prompts using GPT-4 model, and iteratively refining the synthesis process through a structured feedback loop, the Pipeline Designer ensures the generation of high-quality, personalized WordArt.

### 3.2 Glyph Designer Agent

The Glyph Designer is a key component of MetaDesigner, supporting the generation of three glyph types: Normal, Traditional, and Semantic. Normal and traditional glyphs suit formal contexts like weddings and galas, while semantic glyphs are tailored for humorous and creative applications. The GPT-4 selects the appropriate glyph type based on the given prompts.

Normal & Traditional Glyph. For formal scenarios, the Glyph Designer renders normal and traditional glyphs using the FreeType font library:

$$ $\small G_{n},G_{t}=\phi_{n}(s^{\text{gly}}),\phi_{t}(s^{\text{gly}})$ (6) $$

where $\phi_{n}$ and $\phi_{t}$ are the rendering functions for normal and traditional glyphs, respectively, and $s^{\text{gly}}$ is the input prompt.

Semantic Glyph Transformation. The Glyph Designer enhances WordArt typography through semantic glyph transformation, optimizing vector graphs to approximate target objects using differential rasterization and a depth-to-image SD model:

$$ $\small G_{s}=\phi_{s}(s^{\text{sem}},\mathcal{M})$ (7) $$

where $\phi_{s}$ is the semantic glyph transformation function, $s^{\text{sem}}$ is the input prompt, and $\mathcal{M}$ is the model library. The GPT-4 ensures readability by integrating context for full paragraph recognition.

The transformed glyph image $I_{\text{sem}}$ is created from the trainable parameters $\theta$ of the SVG-format character input using DiffVG $\boldsymbol{\phi}(\cdot)$. An optimized and cropped character segment $x$ yields an enhanced image batch $X_{\text{aug}}$. The semantic concept $S$ and $X_{\text{aug}}$ are input into a vision-language model to compute the loss for parameter optimization, applying the SDS loss in the latent space code $z$.

Glyph Style Selection. The choice of glyph type depends on the context, with normal and traditional glyphs used in formal settings and semantic glyphs in creative and humorous contexts. Glyphs play a crucial role in visual communication, influencing readability and the reader’s emotional response.

The overall glyph generation process in the Glyph Designer can be summarized as:

$$ $\small G=\phi(s^{\text{gly}},\mathcal{P}^{\text{gly}},\mathcal{M})=\begin{ cases}\phi_{n}(s^{\text{gly}})&\text{if formal context}\\ \phi_{t}(s^{\text{gly}})&\text{if traditional context}\\ \phi_{s}(s^{\text{sem}},\mathcal{M})&\text{if creative context}\end{cases}$ (8) $$

where $G$ is the generated glyph, $\mathcal{P}^{\text{gly}}$ represents the glyph design hyperparameters, and the glyph type selection ($\phi_{n}$, $\phi_{t}$, $\phi_{s}$) depends on the context derived from the user prompt. The Glyph Designer dynamically selects and renders contextually appropriate glyph styles using advanced techniques and iterative optimization.

Figure: Figure 3: The hierarchical model tree structure facilitates fine-grained ToT (Tree of Thought) model selection. The complete tree structure is available in the supplementary materials.
Refer to caption: x4.png

### 3.3 Texture Designer Agent

The Texture Designer overcomes single-model limitations by integrating controllable image synthesis with the Tree-of-Thought (ToT) framework, catering to diverse user demands while infusing creativity and customization into the design process. The agent is formulated as:

$$ $\displaystyle\small I_{\text{tex}}$ $\displaystyle=\psi^{\text{tex}}(I_{\text{gly}},s^{\text{tex}},\mathcal{F}, \mathcal{M})$ (9) $\displaystyle=\textbf{TexR}(I_{\text{gly}},s^{\text{tex}},C,\textbf{ToTSel}(s^ {\text{tex}},\mathcal{F},\mathcal{M}))$ $$

where $I_{\text{tex}}$ is the final textured image, TexR is the controllable synthesis mechanism, ToTSel is the ToT-informed model selection function, $I_{\text{gly}}$ is the input glyph image, $s^{\text{tex}}$ is the texture prompt, $C$ are control conditions, $\mathcal{F}$ guides the ToT process, and $\mathcal{M}$ is the model library. Next we only give a formulaic description, more details are in the supplementary materials.

(1) Controllable Synthesis. The ControlNet enhances the adaptability and diversity of texture styles by manipulating parameters such as Canny Edge, Depth, Scribble, and original font images:

$$ $\small I_{\text{tex}}=\textbf{TexR}(I_{\text{gly}},s^{\text{tex}},C,\mathcal{W})$ (10) $$

where $\mathcal{W}$ represents the selected model weights, allowing for fine control over texture characteristics.

(2) Tree-of-Thought Selection. The ToT strategy explores various reasoning paths to ensure uniqueness and artistic integrity. It begins with Thought Decomposition and Generation, breaking down prompt $s^{\text{tex}}$ into conceptual pathways $\{z_{1},z_{2},\ldots,z_{n}\}$ guided by $\mathcal{F}(s^{\text{tex}})$. State Evaluation and Model Search then assesses each pathway’s feasibility using heuristic $V(z_{i})$ and searches for the best-fitting model $\mathcal{M}_{\text{best}}$:

$$ $\small\mathcal{M}_{\text{best}}=\arg\max_{\mathcal{M}\in\mathcal{M}_{\text{lib }}}\sum_{i=1}^{n}V(z_{i}|\mathcal{M})$ (11) $$

ensuring the selected model aligns with the user’s creative vision.

(3) Model Library Integration. The 68 LoRA model library, organized into categories like "General," "Realistic," "SCI-FI," "Art," "Design," and "Cartoon," facilitates refined model choices as $\mathcal{M}=\{\mathcal{M}_{\text{gen}},\mathcal{M}_{\text{real}},\mathcal{M}_{
\text{sci}},\mathcal{M}_{\text{art}},\mathcal{M}_{\text{des}},\mathcal{M}_{
\text{car}}\}$. To meet diverse requirements, the Texture Designer merges LoRA models through weighted combinations:

$$ $\mathcal{W}_{\text{fusion}}=\sum_{i}\alpha_{i}\mathcal{W}_{i}$ (12) $$

with $\alpha_{i}$ representing the weights assigned to each LoRA model $\mathcal{W}_{i}$. The unified framework in Eq. 9 captures the transformation of glyph images into textural masterpieces, guided by user inputs and control conditions. This sophisticated and iterative process ensures each texture is uniquely tailored to the user’s vision, resulting in a highly personalized and expressive output.

### 3.4 Q&A Evaluation Agent

We have designed a feedback mechanism for hyperparameter tuning that focuses on four key aspects: text-to-image consistency, image quality, glyph feedback, and user preference. The LLaVA model evaluates text-to-image consistency and image quality, while user studies provide feedback in a Q&A format. Evaluation prompts are generated based on the goals of each synthesized image, and the LLaVA model analyzes these prompts against the generated images, producing feedback metrics: $G_{m}=\{g^{\text{cos}}_{m},g^{\text{qua}}_{m},\mathcal{L}_{m}\}$. GPT-4 summarizes this feedback, integrating the metrics into coherent scores and providing rationales for hyperparameter adjustments.

MetaDesigner also gathers user feedback structured around GPT-4-generated questions to capture user preferences ($g^{\text{pref}}_{u}$) and perceptions of glyph style ($g^{\text{gly}}_{u}$): $G_{u}=\{g^{\text{cos}}_{u},g^{\text{qua}}_{u},g^{\text{gly}}_{u},g^{\text{pref
}}_{u},\mathcal{L}_{u}\}$. Although optional, user feedback is crucial for aligning outputs with user expectations. The final feedback, $G=\text{Merge}(G_{m},G_{u})$, combines insights from multi-modal evaluations and user responses for hyperparameter optimization, with user input prioritized through a voting strategy.

Formally, the optimization strategy focuses on the objective function, capturing the essence of feedback integration: $\mathcal{L}=\mathcal{L}_{m}+\mathcal{L}_{u}$, where $\mathcal{L}$ combines contributions from the LLaVA model ($\mathcal{L}_{m}$) and user feedback ($\mathcal{L}_{u}$). The goal is to optimize the hyperparameters $\mathcal{P}^{\text{gly}}$ and $\mathcal{P}^{\text{tex}}$ for the synthesized image $\hat{I}$ as:

$$ $\small\mathcal{L}_{m}=\underset{\{\mathcal{P}^{\text{gly}},\mathcal{P}^{\text{ tex}}\}}{\mathrm{argmax}}~{}\mathcal{H}(s^{\text{eval}},\hat{I})$ (13) $$

where $s^{\text{eval}}$ is the evaluative prompt, and $\mathcal{H}$ denotes the heuristic from the LLaVA model’s analysis. This process guides the search for optimal hyperparameters, maximizing the quality and relevance of the generated WordArt.

As shown in Alg. 1 and Fig. 4, the adaptive feedback integration and tuning process dynamically adjusts the hyperparameter set $\mathcal{P}$, including pipeline, glyph, and texture design parameters, based on aggregated feedback. User preferences and text feedback primarily influence glyph design parameters ($\mathcal{P}^{\text{gly}}$) and the pipeline strategy, while objective feedback on text-to-image consistency and image quality informs texture design parameters ($\mathcal{P}^{\text{tex}}$). This iterative loop, where $\hat{\mathcal{P}}=\mathcal{F}(G|s^{\text{update}})$ denotes the updated hyperparameter set derived from synthesized feedback $G=\text{Merge}(G_{m},G_{u})$, ensures responsiveness to both quality metrics and user preferences, fostering continuous improvement and customization. More details are in the supplementary materials.

Figure: Figure 4: Illustration of the feedback loop for hyperparameter tuning, showcasing the integration of user preference, glyph quality, text-to-image consistency, and image quality evaluations.
Refer to caption: x5.png

## 4 Experiments

To evaluate the effectiveness of MetaDesigner, we compiled a set of 150 prompts to synthesize WordArt, categorized into five themes: "cartoon," "design," "reality," "sci-fi," and "traditional culture." These prompts were carefully curated to cover a wide range of artistic styles and cultural elements. For the user study, a subset of 20 prompts was selected, encompassing English, Chinese, Japanese, and Korean, to evaluate the system’s multilingual capabilities.

### 4.1 Comparison with SOTA Methods

We conducted comparative experiments against contemporary state-of-the-art (SOTA) techniques, including Stable Diffusion XL (SD-XL) Podell et al. (), TextDiffuser Frans et al. (), TextDiffuser-2 Chen et al. (), Anytext Tuo et al. (), and DALL-E3. These methods were chosen to represent a diverse range of approaches in text-to-image synthesis. The results, depicted in Figure 5, were evaluated in three key aspects:

WordArt Synthesis Performance. As illustrated in Figure 5, SD-XL fails to accurately render text, exhibiting incomplete support for English. The TextDiffuser series performs adequately in English but falls short in Chinese, Korean, and Japanese. Anytext delivers superior results but does not successfully handle Korean and Japanese. DALL-E3 is limited to English support only. In contrast, MetaDesigner demonstrates robust performance across all tested languages, successfully generating high-quality WordArt. More details are in the supplementary material.

Quality and Diversity. The TextDiffuser series generates similar WordArt, restricted by the SD 1.5 model and limited training data distribution. Anytext has more diversity but lower quality. DALL-E3 has better quality but a cinematic and 3D style. MetaDesigner achieves the best diversity in styles, including realistic, cartoon, and 3D, while maintaining high-quality output across all styles.

**Table 1: User study. We present the results from user study conducted in two dimensions, “Text Accuracy” and “Aesthetics & Creativity”. All values are in percent and higher is better.**
| Evaluation Dimension | SDXL | TextDiffuser | TextDiffuser-2 | Anytext | DALLE 3 | Ours |
| --- | --- | --- | --- | --- | --- | --- |
| Text Accuracy | 7.1 | 45.0 | 37.5 | 76.7 | 37.9 | 93.8 |
| Aesthetics & Creativity | 2.3 | 1.4 | 0.9 | 2.3 | 19.5 | 73.6 |

Creativity and Reasonability. SD-XL produces stylistic text unrelated to the meaning. The TextDiffuser series incorporates relevant elements like leaves and a peace dove. Anytext displays a logo design that fails to align with "World Peace." DALL-E3 produces a cinematic-tone 3D WordArt with elements somewhat aligning with the theme but lacking clarity. MetaDesigner intuitively grasps the essence of "World Peace," crafting WordArt with the sun, branch leaves, a peace dove, and additional resonating elements, demonstrating its ability to generate creative and semantically meaningful designs. More details are in the supplementary material.

**Table 2: Quantitative comparison. We compare our method with SOTA methods on the SSIM and LPIPS metrics. $\mathcal{P}$ and $\mathcal{D}$ respectively indicate ground truth images sourced from Promeai and design-related websites. Boldface and \ulunderlining denote the best and second-best results.**
| GT | Metrics | Text | SDXL | TextDiffuser | TextDiffuser-2 | Anytext | DALLE 3 | Ours |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $\mathcal{P}$ |  | Eng. | 0.1254 | \ul0.1860 | 0.1641 | 0.1324 | 0.0834 | 0.2397 |
| SSIM$\uparrow$ | CJK | 0.1853 | 0.1747 | \ul0.2037 | 0.1021 | 0.1401 | 0.2643 |  |
|  | All | 0.1553 | 0.1803 | \ul0.1839 | 0.1172 | 0.1117 | 0.2520 |  |
|  | Eng. | 0.7491 | 0.7652 | \ul0.7441 | 0.7453 | 0.7653 | 0.6910 |  |
| LPIPS$\downarrow$ | CJK | 0.7712 | 0.7970 | 0.7687 | \ul0.7601 | 0.7693 | 0.6846 |  |
|  | All | 0.7602 | 0.7811 | 0.7564 | \ul0.7527 | 0.7673 | 0.6878 |  |
| $\mathcal{D}$ |  | Eng. | 0.1802 | \ul0.2342 | 0.2036 | 0.1669 | 0.1413 | 0.3119 |
| SSIM$\uparrow$ | CJK | 0.1951 | 0.1846 | \ul0.1987 | 0.1073 | 0.1542 | 0.2539 |  |
|  | All | 0.1877 | \ul0.2094 | 0.2012 | 0.1371 | 0.1478 | 0.2829 |  |
|  | Eng. | 0.7993 | 0.8312 | 0.8366 | 0.8495 | 0.7650 | \ul0.7950 |  |
| LPIPS$\downarrow$ | CJK | 0.8023 | 0.8249 | 0.8429 | 0.8437 | 0.7872 | \ul0.7880 |  |
|  | All | 0.8008 | 0.8280 | 0.8397 | 0.8466 | 0.7761 | \ul0.7915 |  |

Figure: Figure 5: WordArt Synthesis Comparison: The first two columns display results for "World Peace" in English. Columns three and four showcase the Chinese rendition of "World Peace". The final two columns present "World Peace" in Korean and Japanese, respectively. The first column displays WordArt generated using the basic prompt "Create a stylish word ’World Peace’ representing its meaning." The rest are generated with more keywords: “Sun, Peace Dove, leaves, cloud".
Refer to caption: x6.png

Quantitative Analysis. We performed a user study evaluating our approach and SOTA techniques across "Text Accuracy" and "Aesthetics & Creativity." A total of 11 participants were involved in the study, ensuring a diverse range of perspectives. As shown in Table 1, our approach demonstrated significant advantages in both dimensions, highlighting its superiority in generating accurate and visually appealing WordArt. Table 2 presents quantitative results using SSIM and LPIPS metrics, further reaffirming the excellence of our approach in terms of readability and aesthetics.

Figure: Figure 6: Comparison of single letter WordArt. Part of the comparison results are copied from DS-Fusion Tanveer et al. (). The prompts are the “Dragon”, and “Plant”, respectively.
Refer to caption: x7.png

Letter-Level Comparison. To evaluate MetaDesigner’s performance at a more granular level, we benchmarked it against Google search, Stable Diffusion Rombach et al. (), DALL-E3, and DS-Fusion Tanveer et al. (). As shown in Figure 6, Stable Diffusion struggles to generate eligible WordArts comprehensively. DS-Fusion produces superior outcomes with shape deformation and legibility but lacks variety. DALL-E3 delivers high-quality renderings focused on textural representation. MetaDesigner generates WordArt with greater stylistic creativity and maintains high detail quality, demonstrating its ability to create visually striking and diverse designs at the letter level. More examples are in the supplementary material.

### 4.2 Effect of Tree-of-Thought

Figure: Figure 7: The evaluation of the synthesis WordArt. From left to right are the “Relevance", “Quality", and “Style" scores generated by the ChatGPT-4 in the sub-categories. (The performance of ToT+Gemini and ToT+GPT4 are very close)
Refer to caption: x8.png

To verify the effectiveness of the Tree-of-Thought (ToT) scheme, we conducted a quantitative analysis comparing the ControlNet and the ToT-LoRA+ControlNet approaches. GPT-4, a state-of-the-art language model, was used to measure the "Relevance," "Quality," and "Style" of the synthesized WordArt. The results, illustrated in Figure 7, show significant improvements achieved by the ToT-LoRA in all metrics, highlighting its ability to generate WordArt that is highly relevant to the given prompts, visually appealing, and stylistically diverse.

Figure: Figure 8: WordArt Texture rendering
Refer to caption: x9.png

A case study, presented in Figure 8, demonstrates that the ToT-LoRA scheme significantly outperforms ControlNet in WordArt style and text-to-image consistency. Specifically, ToT-LoRA+ControlNet excels in rendering complex textures and maintaining coherence with the given prompts, such as "ethnic customs, New Year, dumplings, steamed bread, kitchen, mother, little boy" and "Renaissance style, Castle." The results show clearer thematic representation and enhanced visual appeal, further validating the effectiveness of our approach. More examples are in the supplementary material.

### 4.3 Effect of Optimization

To illustrate the impact of the optimization process, we conducted a detailed case study, presented in Figure 9. The LLaVA system is employed to identify objects mentioned in the prompt but absent in the generated WordArt. This information is then used to update the generation process, incorporating the omitted elements. Techniques such as symbol enhancement, word sequencing adjustments, and keyword repetition are employed to augment the WordArt generation process, ensuring that the final output accurately reflects the input prompt.
As shown in Figure 9, the optimization process proceeds through several steps to achieve better alignment with the prompts. In Step 1, the initial generation may miss key elements such as "little girl" in the prompt "old man, cake, candles, little girl." In Step 2, iterative refinement introduces missing elements, enhancing text-to-image consistency. Similarly, for the prompt "kitchen, girl, steamed bread, a plate of fruit," the optimization process adds missing objects and refines their depiction over multiple steps.
This optimization process plays a crucial role in enhancing the semantic consistency and visual quality of the generated WordArt.

Figure: Figure 9: The examples are the optimization of the text-to-image consistency.
Refer to caption: x10.png

### 4.4 WordArt Dataset

To further advance the WordArt community and support ongoing research in this domain, we have meticulously curated a Creative and Diverse WordArt Dataset using our approach. This comprehensive dataset encompasses a total of 5,000 multilingual images, including representations from English, Chinese, Japanese, and Korean, spanning a wide array of artistic styles and cultural elements. By providing such a rich and varied resource, we aim to empower researchers and practitioners in the field of artistic typography, enabling them to explore novel techniques, evaluate existing methods, and push the boundaries of WordArt generation.

## 5 Conclusion

In this work, we introduce MetaDesigner, a revolutionary framework for synthesizing artistic typography by leveraging the strengths of Large Language Models (LLMs) to drive a user-centric design paradigm. At the core of MetaDesigner is a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively enable customized WordArt creations, from semantic enhancements to the imposition of complex textures.
By merging generative AI with artistic typography, MetaDesigner represents a significant advancement in the field, democratizing typography design and enriching the digital art domain. The system’s ability to understand user preferences and generate contextually relevant WordArt sets it apart from existing solutions, making it a valuable tool for both professionals and enthusiasts.
Looking ahead, we envision MetaDesigner as a catalyst for further innovation in the intersection of AI and artistic expression. Future work could explore expanding the system’s capabilities to include more complex typographic manipulations, integration with 3D design tools, or adaptation to dynamic, real-time typography applications.

## Appendix A Appendix: More Details of Our Work

This appendix provides more details
of our work that cannot be expanded in the main text.
The main content can be summarized as follows:

1. More Details of ToT Model Selection, appendix B.

(a) Tree-of-Though Model Selection Prompt, section B.1.

(b) Tree-of-Though Model Selection Case , section B.2.

2. More Details of Image Evaluation appendix C.

(a) ToT-LoRA Evaluation Prompt, section C.1.

(b) LLaVA Evaluation Prompt , section C.2.

3. More Details of Dataset appendix D.

## Appendix B More Details of ToT Model selection

Figure: Figure 10: Hierarchical model tree with multi-level sub-categories enabling fine-grained ToT (Tree of Thought) model selection for diverse WordArt generation.
Refer to caption: x11.png

### B.1 ToT Model Selection Template

*You know the cultures of different countries and different types of art styles. Identify and analyze aspects of The characteristics of vocabulary, theme, content, Implicit culture, and Reader perception. Then from each aspect of the analysis, you must select an element from the input list that best matches the input prompt.
All analyses will be written down and then shared with the group. You then analyze all analyses and output the consensus-selected element or your best guess-matched element. The final selected word MUST be the same as the TEMPLATE:*

TEMPLATE:

Selected: [the selected word]

Input list: {search_list}

Input prompt: {input}

### B.2 ToT Model Selection Case

*Input Prompt: A girl, a boy, in a church*

- •
Thoughts-1st: Analyzing the given prompt with keywords for selection: "General", "Traditional Art", "Design", "Cartoon", "SCI-FI", "Realistic"
–
Vocabulary: The vocabulary used in the prompt is quite general and straightforward. It mentions a girl, a boy, and a church, which are common words and do not necessarily indicate a specific art style or culture.
–
Theme: The theme seems to be centered around a girl and a boy in a church. This could suggest a religious or spiritual theme, which is often depicted in traditional art.
–
Content: The content is quite simple and does not provide much detail. However, the mention of a church could suggest a religious or spiritual context, which is often associated with traditional art.
–
Implicit Culture: The mention of a church could suggest a culture where Christianity is prevalent. Many traditional art pieces are influenced by religious themes, especially Christianity.
–
Reader Perception: The reader might perceive this as a simple scene of a girl and a boy in a church. Depending on their cultural background and personal experiences, they might associate this with traditional art.
–
Summarization: After analyzing all aspects, the element from the input list that best matches the input prompt is ’Traditional Art’.
–
Selected: Traditional Art
- •
Thoughts-2nd: Analyzing the given prompt with keywords: "European Style", "Chinese Style" for selection:
–
Vocabulary: The words used are simple and straightforward. They refer to two individuals, a girl and a boy, and a location, a church. The vocabulary does not directly indicate a specific culture or art style.
–
Theme: The theme seems to be about two individuals in a religious setting. This could be interpreted in many ways, but it does not directly indicate a specific culture or art style.
–
Content: The content is minimal and does not provide much context. However, the mention of a church might suggest a more Western or European context, as churches are more commonly associated with Western religions.
–
Implicit Culture: The implicit culture could be inferred from the mention of a church. Churches are more commonly found in European cultures, suggesting a European style.
–
Reader Perception: The reader might perceive this as a scene from a European setting due to the mention of a church. However, this is subjective and could vary depending on the reader’s background and experiences.
–
Summarization: After analyzing all aspects, the element from the input list that best matches the input prompt is ‘European Style’.
–
Selected: European style
- •
Thoughts-3rd: Analyzing the given prompt with keywords: "Painting", "Engrave" for selection:
–
Vocabulary: The input prompt uses simple, everyday language. The words "girl," "boy," and "church" are common and straightforward, suggesting a scene that is easy to visualize.
–
Theme: The theme seems to be centered around a religious or spiritual setting, given the mention of a church. It could also suggest a sense of community or family, as it involves a girl and a boy.
–
Content: The content is quite minimalistic, focusing on two characters within a specific setting. There’s no explicit action or event mentioned, leaving much to the reader’s interpretation.
–
Implicit Culture: The mention of a church suggests a culture where Christianity is prevalent. It could also imply a traditional or conservative society where church attendance is common.
–
Reader Perception: The reader might perceive this as a peaceful, serene scene. Depending on their personal experiences and cultural background, they might also associate it with feelings of spirituality, community, or nostalgia.
–
Summarization: all these aspects, the scene seems more suited to a ’Painting’ rather than an ’Engrave’. A painting can capture the subtleties of the scene, the characters, and the setting in a more vivid and colorful way.
–
Selected: Painting
- •
Thoughts-3rd: Analyzing the given prompt with keywords: "Oil Painting", "Van Gogh", "Monet", "RealisticOilPainting" for selection:
–
Vocabulary: The vocabulary used in the prompt is simple and straightforward. It describes a scene involving two individuals in a specific location. There is no specific artistic term used that could directly link to any of the elements in the input list.
–
Theme: The theme seems to be a common scene or event, possibly a religious or spiritual one given the setting is a church. This could potentially link to ’Oil Painting’ or ’RealisticOilPainting’ as these art styles often depict everyday life and common scenes.
–
Content: The content is a depiction of two individuals in a church. This could be a scene from everyday life, a religious event, or a spiritual gathering. This could potentially link to ’Oil Painting’ or ’RealisticOilPainting’ as these art styles often depict such scenes.
–
Implicit Culture: The culture implied here could be a Christian or Western culture, given the setting of a church. This could potentially link to ’Van Gogh’ or ’Monet’ as these artists were from the Western world and often depicted scenes from their culture.
–
Reader Perception: The reader might perceive this as a simple, everyday scene or a depiction of a religious event. This could potentially link to ’Oil Painting’ or ’RealisticOilPainting’ as these art styles often depict such scenes.
–
Summarization: After analyzing all aspects, the element that best matches the input prompt from the input list is ’Oil Painting’. This is because the prompt describes a scene that could easily be depicted in this art style, and it does not specifically link to any particular artist or a more specific style of oil painting.
–
Selected: Oil Painting

Figure: Algorithm 1 Hyperparameter Tuning

Figure: Figure 11: Impact of LoRA fusion with varying weights. Top: Merging Chinese Bronzes and Sketch Style LoRAs. Bottom: Combining Comic and Croissant Style LoRAs.
Refer to caption: x12.png

Figure: Figure 12: The Comparison of WordArt texture rendering on the glyph “World Peace" (ControlNet vs. ToT-GPT4-LoRA+ControlNet).
Refer to caption: x13.png

Figure: Figure 13: The Comparison of WordArt texture rendering on the glyph “World Peace" (ControlNet vs. ToT-GPT4-LoRA+ControlNet).
Refer to caption: x14.png

Figure: Figure 14: Diverse examples of WordArt generated by our method. Supports multiple languages (Chinese, English, Japanese, Korean, Arabic numerals, etc.) and various word counts. Applications span e-commerce marketing, film and entertainment, personalized social networking, and more. The method adapts to different styles, textures, and themes, showcasing versatility across cultural and linguistic contexts. Enlarge for detailed viewing of intricate designs and multilingual rendering.
Refer to caption: x15.png

## Appendix C More Details of Image Evaluation

### C.1 ToT-LoRA Evaluation Prompt

"You are now a scoring judge for a generative model. I will provide some images along with prompts generated by the model. Now, assess the relevance of the image content to the prompts and rate the overall quality of these generated images on a scale of 1-10. Reply with the Prompt Relevance scores for the prompt word and image relevance, the Image Quality, and Style and Prompt Match score between the image style and prompt word. Provide only numerical scores without any explanations."

### C.2 LLaVA Evaluation Prompt

You have to decouple the <Input sentence> into individual, Real world Targets. The target must be something visible and tangible in the real world. The decoupled Targets MUST be the same as the TEMPLATE:

TEMPLATE:

Targets:{target,target}

<Input sentence>: {input}

Subsequently, we employ an iterative querying manner, systematically verifying the presence of each object via the LLaVA:

Is {target} exist in the photo? Just answer Yes or No.

## Appendix D More Details of WordArt Dataset

To evaluate the capabilities of our proposed MetaDesigner framework, we assembled a comprehensive dataset comprising 150 distinct prompts specifically tailored for synthesizing WordArt. These prompts are categorized into five broad themes, namely "cartoon", "design", "reality", "sci-fi", and "traditional culture", to cover a wide spectrum of artistic styles and thematic elements. This thematic diversity ensures that our dataset not only supports but also challenges the MetaDesigner in generating WordArt that is both aesthetically pleasing and thematically appropriate across various artistic genres.

Multilingual Scope and Selection Criteria.
For our user study, we employed a curated subset of 20 prompts to generate WordArt using the proposed comparison methods. This subset was designed to showcase a rich linguistic tapestry, encompassing English, Chinese, Japanese, and Korean, thereby rigorously evaluating MetaDesigner’s adeptness in processing and rendering multilingual inputs. The selection process adhered to a multifaceted set of criteria, including each prompt’s capacity to represent the thematic and linguistic diversity of the broader dataset, its potential relevance to real-world applications across domains such as e-commerce, education, digital marketing, and creative content production, and its ability to challenge and showcase the system’s multilingual capabilities. Additionally, we considered each prompt’s cultural resonance and significance within diverse global contexts, ensuring an inclusive approach. This carefully crafted selection enables a comprehensive and nuanced assessment of MetaDesigner’s performance across various linguistic and cultural landscapes, providing valuable insights into its versatility and adaptability.

Dataset Composition and Thematic Balance.
The dataset exhibits a balanced distribution across the 5 themes, with each theme, with each theme precisely encompassing 30 carefully crafted prompts. This equitable representation ensures a diverse range of styles and thematic content, enabling a thorough assessment of MetaDesigner’s capabilities. Prompt selection was guided by multiple criteria, including cultural significance, prominence in contemporary digital art, and relevance to potential commercial and educational applications. This careful curation process enhances the dataset’s utility for evaluating AI-driven artistic typography across various contexts and use cases.

Applicability and Use Cases.
The dataset’s versatility is exemplified through a comprehensive array of cases, as illustrated in Figure 14. These examples span a diverse range, including cartoon text, Chinese surnames, and Chinese solar terms, demonstrating the dataset’s capacity to support a wide spectrum of artistic and linguistic expressions. This diversity not only validates MetaDesigner’s proficiency in generating contextually and culturally relevant WordArt but also underscores the dataset’s extensive applicability across various domains. The potential applications are far-reaching: from enhancing consumer engagement in e-commerce through customized WordArt promotions, to facilitating language learning and cultural education via multilingual WordArt in educational settings.

Figure: Figure 15: Numerical analyses on our WordArt dataset. The x-axis of the histogram denotes the number of images, measured in thousands, whereas the y-axis represents the specific categories.
Refer to caption: x16.png

Figure: Figure 16: Numerical analyses on user preference. The x-axis of the histogram denotes the number of images, measured in thousands, whereas the y-axis represents the specific categories. "0" in "Number of Characters" part denotes that the user uploaded a mask image instead of inputting texts.
Refer to caption: x17.png

Quantitative Analysis and User Preferences.
Our comprehensive analysis of the WordArt dataset reveals significant insights, as illustrated in Figure 15. The "Image Plaza" now houses over two million images, showcasing a diverse range of image ratios, word counts, and thematic content. Interested parties can access and explore these images through our WordArt space at [https://modelscope.cn/studios/WordArt/WordArt/summary](https://modelscope.cn/studios/WordArt/WordArt/summary).
Additionally, we’ve conducted an in-depth examination of user preferences, as depicted in Figure 16. To date, our platform has facilitated the creation of more than one million user-generated images. The data indicates a strong user preference for the 16:9 image aspect ratio, with most inputs consisting of five characters or fewer. Notably, users demonstrate a marked inclination towards images featuring an alpha channel, highlighting a preference for designs with transparency or layering capabilities.

## Peer Reviews

confidence: 4
contribution: 4
initial_rating: 6
presentation: 4
questions: **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.
soundness: 3
strengths: **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.
summary: 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.
weaknesses: **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.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: 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?
soundness: 2
strengths: 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.
summary: 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.
weaknesses: 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.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 2
questions: 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?
soundness: 2
strengths: 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.
summary: 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.
weaknesses: 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.

Meta-review: This paper introduces a multi-agent framework for artistic typography synthesis, consisting of a Pipeline Designer, a Glyph Designer, and a Texture Designer.

The major strengths of the paper: 1) The proposed framework is well motivated and designed, with multi agents and LLM and advanced prompting.  2) It can generate artistic typograph with higher visual quality, text representation, and style, as well as better alignment to user preference, and multi lingual user cases. 3) The whole amount work/contribution is non-trival, and some promising results are shown.

The major weaknesses of the paper: 1) As reviewers pointed out, this is a system work, so the technical contribution itself may not be very high. And the system is quite complicated. 2) Some technical and experiment details are missing or not clear.

Decision: Accept (Poster)