We lifted 6 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| posterllava/posterllava | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| expand2square | Ran | posterllava/posterllava/llava/mm_utils.py pointer only (licence: NONE) · get_code("592b3c1a88f93d7c") |
| get_chunk | Ran | posterllava/posterllava/llava/eval/model_vqa.py pointer only (licence: NONE) · get_code("42a46570620cd9fa") |
| is_none | Ran | posterllava/posterllava/llava/eval/model_vqa_mmbench.py pointer only (licence: NONE) · get_code("bae18947b56f2be1") |
| load_image_from_base64 | Ran | posterllava/posterllava/llava/mm_utils.py pointer only (licence: NONE) · get_code("c3ee9d07c900dd55") |
| split_list | Ran | posterllava/posterllava/llava/eval/model_vqa.py pointer only (licence: NONE) · get_code("076c252c52cbb161") |
| process_images | Not yet run | posterllava/posterllava/llava/mm_utils.py pointer only (licence: NONE) · get_code("344dff4791fd1381") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Layout generation is the keystone in achieving automated graphic design, requiring arranging the position and size of various multi-modal design elements in a visually pleasing and constraint-following manner. Previous approaches are either inefficient for large-scale applications or lack flexibility for varying design requirements. Our research introduces a unified framework for automated graphic layout generation, leveraging the multi-modal large language model (MLLM) to accommodate diverse design tasks. In contrast, our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts under specific visual and textual constraints, including user-defined natural language specifications. We conducted extensive experiments and achieved state-of-the-art (SOTA) performance on public multi-modal layout generation benchmarks, demonstrating the effectiveness of our method. Moreover, recognizing existing datasets' limitations in capturing the complexity of real-world graphic designs, we propose two new datasets for much more challenging tasks (user-constrained generation and complicated poster), further validating our model's utility in real-life settings. Marking by its superior accessibility and adaptability, this approach further automates large-scale graphic design tasks. Finally, we develop an automated text-to-poster system that generates editable SVG posters based on users' design intentions, bridging the gap between layout generation and real-world graphic design applications. This system integrates our proposed layout generation method as the core component, demonstrating its effectiveness in practical scenarios. The code and datasets are open-sourced on https://github.com/posterllava/PosterLLaVA.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.02884")
get_code_for_paper("2406.02884")
have("2406.02884")
Connect an agent — have() is free.