TikZero: Zero-Shot Text-Guided Graphics Program Synthesis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913989977440256 |
|---|---|
| author | Belouadi, Jonas Ilg, Eddy Keuper, Margret Tanaka, Hideki Utiyama, Masao Dabre, Raj Eger, Steffen Ponzetto, Simone Paolo |
| author_facet | Belouadi, Jonas Ilg, Eddy Keuper, Margret Tanaka, Hideki Utiyama, Masao Dabre, Raj Eger, Steffen Ponzetto, Simone Paolo |
| contents | Automatically synthesizing figures from text captions is a compelling capability. However, achieving high geometric precision and editability requires representing figures as graphics programs in languages like TikZ, and aligned training data (i.e., graphics programs with captions) remains scarce. Meanwhile, large amounts of unaligned graphics programs and captioned raster images are more readily available. We reconcile these disparate data sources by presenting TikZero, which decouples graphics program generation from text understanding by using image representations as an intermediary bridge. It enables independent training on graphics programs and captioned images and allows for zero-shot text-guided graphics program synthesis during inference. We show that our method substantially outperforms baselines that can only operate with caption-aligned graphics programs. Furthermore, when leveraging caption-aligned graphics programs as a complementary training signal, TikZero matches or exceeds the performance of much larger models, including commercial systems like GPT-4o. Our code, datasets, and select models are publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TikZero: Zero-Shot Text-Guided Graphics Program Synthesis Belouadi, Jonas Ilg, Eddy Keuper, Margret Tanaka, Hideki Utiyama, Masao Dabre, Raj Eger, Steffen Ponzetto, Simone Paolo Computation and Language Computer Vision and Pattern Recognition Automatically synthesizing figures from text captions is a compelling capability. However, achieving high geometric precision and editability requires representing figures as graphics programs in languages like TikZ, and aligned training data (i.e., graphics programs with captions) remains scarce. Meanwhile, large amounts of unaligned graphics programs and captioned raster images are more readily available. We reconcile these disparate data sources by presenting TikZero, which decouples graphics program generation from text understanding by using image representations as an intermediary bridge. It enables independent training on graphics programs and captioned images and allows for zero-shot text-guided graphics program synthesis during inference. We show that our method substantially outperforms baselines that can only operate with caption-aligned graphics programs. Furthermore, when leveraging caption-aligned graphics programs as a complementary training signal, TikZero matches or exceeds the performance of much larger models, including commercial systems like GPT-4o. Our code, datasets, and select models are publicly available. |
| title | TikZero: Zero-Shot Text-Guided Graphics Program Synthesis |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.11509 |