title: TikZero: Zero-Shot Text-Guided Graphics Program Synthesis
summary: With the rise of generative AI, synthesizing figures from text captions
  becomes a compelling application. 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.
paper_authors._id: 67d7e187ee9f6c22e2b1fa0a
paper_authors.name: Jonas Belouadi
paper_authors._id: 67d7e187ee9f6c22e2b1fa0b
paper_authors.name: Eddy Ilg
paper_authors._id: 67d7e187ee9f6c22e2b1fa0c
paper_authors.name: Margret Keuper
paper_authors._id: 67d7e187ee9f6c22e2b1fa0d
paper_authors.name: Hideki Tanaka
paper_authors._id: 67d7e187ee9f6c22e2b1fa0e
paper_authors.name: Masao Utiyama
paper_authors._id: 67d7e187ee9f6c22e2b1fa0f
paper_authors.name: Raj Dabre
paper_authors._id: 67d7e187ee9f6c22e2b1fa10
paper_authors.name: Steffen Eger
paper_authors._id: 67d7e187ee9f6c22e2b1fa11
paper_authors.name: Simone Paolo Ponzetto
paper_title: TikZero: Zero-Shot Text-Guided Graphics Program Synthesis
paper_summary: With the rise of generative AI, synthesizing figures from text captions
  becomes a compelling application. 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.
paper_ai_summary: TikZero uses image representations as a bridge to synthesize graphics programs from text captions, outperforming baselines and matching larger models in zero-shot and supervised settings.
paper_ai_keywords: generative AI TikZ graphics programs captioned raster images image representations text-guided graphics synthesis zero-shot synthesis