While paper instructions are one of the mainstream medium for sharing knowledge, consuming such instructions and translating them into activities are inefficient due to the lack of connectivity with physical environment. We present PaperToPlace, a novel workflow comprising an authoring pipeline, which allows the authors to rapidly transform and spatialize existing paper instructions into MR experience, and a consumption pipeline, which computationally place each instruction step at an optimal location that is easy to read and do not occlude key interaction areas. Our evaluations of the authoring pipeline with 12 participants demonstrated the usability of our workflow and the effectiveness of using a machine learning based approach to help extracting the spatial locations associated with each steps. A second within-subject study with another 12 participants demonstrates the merits of our consumption pipeline by reducing efforts of context switching, delivering the segmented instruction steps and offering the hands-free affordances.

翻译：虽然纸质指令是分享知识的主流媒介之一，但由于缺乏与物理环境的连接，人们读取此类指令并将其转化为具体行为的效率较低。我们提出PaperToPlace这一新型工作流，包含一条创作管道（允许作者快速将现有纸质指令转化并空间化为混合现实体验），以及一条消费管道（通过计算将每个指令步骤放置在便于阅读且不遮挡关键交互区域的最佳位置）。我们对12名参与者的创作管道评估结果证明了该工作流的可用性，以及基于机器学习的方法在辅助提取各步骤空间位置方面的有效性。另一项针对12名参与者的受试者内实验表明，我们的消费管道通过减少情境切换负担、提供分步指令呈现以及免手持交互能力，展现了其优势。