Workflows are a fundamental component of automation in enterprise platforms, enabling the orchestration of tasks, data processing, and system integrations. Despite being widely used, building workflows can be complex, often requiring manual configuration through low-code platforms or visual programming tools. To simplify this process, we explore the use of generative foundation models, particularly vision-language models (VLMs), to automatically generate structured workflows from visual inputs. Translating hand-drawn sketches or computer-generated diagrams into executable workflows is challenging due to the ambiguity of free-form drawings, variations in diagram styles, and the difficulty of inferring execution logic from visual elements. To address this, we introduce StarFlow, a framework for generating structured workflow outputs from sketches using vision-language models. We curate a diverse dataset of workflow diagrams -- including synthetic, manually annotated, and real-world samples -- to enable robust training and evaluation. We finetune and benchmark multiple vision-language models, conducting a series of ablation studies to analyze the strengths and limitations of our approach. Our results show that finetuning significantly enhances structured workflow generation, outperforming large vision-language models on this task.
View on arXiv@article{bechard2025_2503.21889,
title={ StarFlow: Generating Structured Workflow Outputs From Sketch Images },
author={ Patrice Bechard and Chao Wang and Amirhossein Abaskohi and Juan Rodriguez and Christopher Pal and David Vazquez and Spandana Gella and Sai Rajeswar and Perouz Taslakian },
journal={arXiv preprint arXiv:2503.21889},
year={ 2025 }
}