Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors. In this work, we attempt at building a holistic model that can jointly solve many different design tasks. Our model, which we denote by FlexDM, treats vector graphic documents as a set of multi-modal elements, and learns to predict masked fields such as element type, position, styling attributes, image, or text, using a unified architecture. Through the use of explicit multi-task learning and in-domain pre-training, our model can better capture the multi-modal relationships among the different document fields. Experimental results corroborate that our single FlexDM is able to successfully solve a multitude of different design tasks, while achieving performance that is competitive with task-specific and costly baselines.

翻译：生成图形文档的创意工作流涉及复杂的相互关联任务，例如对齐元素、选择合适的字体或采用美观和谐的色彩。本文尝试构建一个能够联合解决多种不同设计任务的整体模型。我们提出的模型FlexDM将矢量图形文档视为一组多模态元素，并利用统一架构学习预测元素类型、位置、样式属性、图像或文本等掩码字段。通过显式多任务学习和领域内预训练，该模型能更好地捕捉不同文档字段间的多模态关系。实验结果表明，单一模型FlexDM能够成功解决多种设计任务，同时其性能可与专用且成本高昂的基线模型相媲美。