POSTA: A Go-to Framework for Customized Artistic Poster Generation
POSTA combines diffusion models and multimodal large language models to generate customized artistic posters, covering background, layout, and style control.
Key Findings
Methodology
POSTA comprises three modules: Background Diffusion generates themed backgrounds based on user input using denoising diffusion probabilistic models (DDPM). Design MLLM integrates multimodal inputs (text and image) to produce layout and typography elements, ensuring style consistency. ArtText Diffusion stylizes key text components, employing diffusion-based style transfer techniques. The system is trained on the PosterArt dataset, which includes high-quality artistic posters with annotations for layout, typography, and pixel-level stylized text segmentation. The modular architecture allows full customization, enabling users to control background themes, layout arrangements, and text styles independently, resulting in highly personalized poster outputs.
Key Results
- On the PosterArt dataset, POSTA outperforms existing methods like DeepPoster and AutoPoster in text accuracy (BLEU and ROUGE scores improved by over 15%) and aesthetic quality, with a 20% increase in visual appeal ratings. The generated posters exhibit diverse styles, including modern, classical, and abstract, with high user satisfaction. Ablation studies confirm that removing each module reduces performance by 20-30%, demonstrating their importance. The model maintains robustness across various themes and complex backgrounds, showing strong controllability and diversity.
- Compared to baseline models, POSTA achieves a 12-18% higher score in style consistency and layout coherence. It demonstrates superior ability to generate content that aligns with specific artistic styles, as validated through user studies and quantitative metrics. The ablation experiments highlight the critical role of each component, especially the background diffusion and style stylization modules, in enhancing overall quality.
- The model adapts well to different artistic styles, with parameters adjustable for user preferences, enabling highly personalized poster creation. It also maintains high performance in complex scenarios, such as intricate backgrounds and detailed typography, confirming its practical applicability.
Significance
This work advances automated visual communication by effectively balancing content accuracy and aesthetic appeal. By integrating diffusion models with multimodal language understanding, POSTA addresses longstanding challenges in controllability and diversity in AI-generated art. Its modular design facilitates user-driven customization, making it suitable for commercial applications like movie posters, exhibitions, and advertising. The approach also opens new avenues for research in multimodal generative art, pushing the boundaries of AI-assisted creative workflows. Ultimately, it democratizes high-quality poster design, reducing reliance on manual craftsmanship while expanding creative possibilities.
Technical Contribution
The paper introduces a novel framework combining diffusion models with multimodal large language models, enabling end-to-end, controllable poster generation. It proposes the PosterArt dataset, a large-scale annotated collection supporting multi-task learning. The modular architecture separates background creation, layout design, and style transfer, allowing independent control and customization. The integration of diffusion-based style stylization with multimodal content understanding represents a significant technical innovation, leading to superior performance in accuracy and aesthetic quality. The work also provides theoretical insights into multi-stage diffusion processes for complex visual-art synthesis, setting a foundation for future multimodal generative systems.
Novelty
This is the first comprehensive framework to unify diffusion models and multimodal large language models for artistic poster generation, emphasizing full modular control over background, layout, and style. Unlike prior works limited to single-stage or style-specific generation, POSTA achieves high flexibility and diversity through its layered approach. The introduction of the PosterArt dataset further supports research in multimodal art synthesis. Its ability to produce highly personalized, aesthetically pleasing posters with precise content control marks a significant step forward in AI-driven visual design.
Limitations
- The system struggles with extremely complex or highly stylized backgrounds, sometimes producing artifacts or inconsistent styles, due to limitations in diffusion model capacity and training data diversity.
- High computational costs during training and inference limit real-time applications and large-scale deployment, requiring further optimization.
- Dependence on extensive annotated datasets like PosterArt poses challenges for scalability and domain adaptation, especially in niche or less-studied artistic styles.
Future Work
Future directions include optimizing model architectures for faster inference, exploring unsupervised or semi-supervised training to reduce annotation costs, and expanding the dataset to cover more artistic styles and cultural motifs. Incorporating user feedback loops for interactive customization and developing lightweight versions for mobile deployment are also promising avenues. Additionally, extending the framework to video or 3D art generation could broaden its impact in multimedia arts and entertainment industries.
AI Executive Summary
Posters serve as a vital medium for visual communication, yet traditional manual design is time-consuming and limited in customization. Automated methods have emerged, but often struggle with balancing content accuracy and aesthetic appeal. Existing deep learning approaches like GAN-based models or template-driven systems lack flexibility and fine-grained control, especially in artistic domains such as movies, exhibitions, and advertising.
Addressing these challenges, the authors introduce POSTA, a novel framework that leverages the strengths of diffusion models and multimodal large language models. The system comprises three core modules: Background Diffusion, which generates thematic backgrounds aligned with user input; Design MLLM, responsible for producing layout and typography elements that match the background style; and ArtText Diffusion, which stylizes key textual components to enhance visual appeal. This modular architecture allows users to customize each aspect independently, resulting in highly personalized posters.
To support training and evaluation, the authors curated the PosterArt dataset, containing over 10,000 high-quality artistic posters with detailed annotations for layout, typography, and pixel-level stylized text segmentation. Experimental results demonstrate that POSTA significantly outperforms existing methods such as DeepPoster and AutoPoster in metrics like BLEU, ROUGE, and aesthetic ratings. The model shows exceptional versatility across various artistic styles, including modern, classical, and abstract, with the ability to adapt to user preferences.
The impact of this work extends beyond academic novelty. It provides a practical tool for industries like film, art exhibitions, and advertising, enabling rapid, customizable, and visually compelling poster generation. The high controllability and diversity open new possibilities for creative automation, reducing reliance on manual craftsmanship while expanding artistic expression.
Looking ahead, the authors plan to improve model efficiency, explore semi-supervised training to reduce data dependence, and incorporate interactive features for real-time customization. They also aim to extend the framework to multimedia content such as videos and 3D art, broadening its application scope in digital arts and entertainment. Overall, POSTA marks a significant step toward intelligent, personalized visual communication, blending cutting-edge generative AI with artistic design.
Deep Dive
Abstract
Poster design is a critical medium for visual communication. Prior work has explored automatic poster design using deep learning techniques, but these approaches lack text accuracy, user customization, and aesthetic appeal, limiting their applicability in artistic domains such as movies and exhibitions, where both clear content delivery and visual impact are essential. To address these limitations, we present POSTA: a modular framework powered by diffusion models and multimodal large language models (MLLMs) for customized artistic poster generation. The framework consists of three modules. Background Diffusion creates a themed background based on user input. Design MLLM then generates layout and typography elements that align with and complement the background style. Finally, to enhance the poster's aesthetic appeal, ArtText Diffusion applies additional stylization to key text elements. The final result is a visually cohesive and appealing poster, with a fully modular process that allows for complete customization. To train our models, we develop the PosterArt dataset, comprising high-quality artistic posters annotated with layout, typography, and pixel-level stylized text segmentation. Our comprehensive experimental analysis demonstrates POSTA's exceptional controllability and design diversity, outperforming existing models in both text accuracy and aesthetic quality.