Virtual reality has proved to be useful in applications in several fields
ranging from gaming, medicine, and training to development of interfaces that
enable human-robot collaboration. It empowers designers to explore applications
outside of the constraints posed by the real world environment and develop
innovative solutions and experiences. Hand gestures recognition which has been
a topic of much research and subsequent commercialization in the real world has
been possible because of the creation of large, labelled datasets. In order to
utilize the power of natural and intuitive hand gestures in the virtual domain
for enabling embodied teleoperation of collaborative robots, similarly large
datasets must be created so as to keep the working interface easy to learn and
flexible enough to add more gestures. Depending on the application, this may be
computationally or economically prohibitive. Thus, the adaptation of trained
deep learning models that perform well in the real environment to the virtual
may be a solution to this challenge. This paper presents a systematic framework
for the real to virtual adaptation using limited size of virtual dataset along
with guidelines for creating a curated dataset. Finally, while hand gestures
have been considered as the communication mode, the guidelines and
recommendations presented are generic. These are applicable to other modes such
as body poses and facial expressions which have large datasets available in the
real domain which must be adapted to the virtual one.

虚拟现实在多个领域中得到了证明：从游戏、医学和培训到人机协作界面的开发，它能够帮助设计师在超越真实环境限制的情况下探索应用，并开发创新的解决方案和体验。对于在虚拟领域借助自然而直观的手势实现协同机器人操作，需要创建大量的数据集，以便使工作界面易于学习和灵活添加手势。由于计算或经济限制，这可能具有一定的挑战性，因此将在真实环境中表现良好的深度学习模型调整到虚拟领域可能是解决方案。本文提出了一个系统的框架，通过虚拟数据集的有限大小实现从真实到虚拟的适应，并提供了创建策划数据集的指导方针。最后，虽然手势被视为通信模式，但所提供的指南和建议是通用的，适用于其他模式，如身体姿势和面部表情，这些模式在真实领域中有大量可用的数据集，需要适应到虚拟领域。