Grunnleggende konsepter
PosterLlama is a novel model that generates visually and textually coherent poster layouts by reformatting layout elements into HTML code and leveraging the design knowledge embedded within language models. It also employs a depth-based augmentation strategy to enhance the robustness of the generated layouts.
Sammendrag
The paper introduces PosterLlama, a model for generating content-aware poster layouts. Key highlights:
PosterLlama reformats layout elements into HTML code to leverage the design knowledge embedded in language models, enabling semantically rich layout generation.
It employs a two-stage training process to connect the visual encoder with the language model, ensuring the model considers both visual and textual content.
To address the challenge of limited poster dataset size, the paper proposes a depth-based augmentation method that focuses on the presence of salient objects.
Extensive evaluations demonstrate that PosterLlama outperforms existing methods in producing authentic and content-aware layouts, supporting a wide range of conditional generation tasks.
The paper also introduces a pipeline for generating advertisement posters that utilizes a scene-text generation module.