2D Key-point estimation is an important precursor to 3D pose estimation
problems for human body and hands. In this work, we discuss the data,
architecture, and training procedure necessary to deploy extremely efficient
2.5D hand pose estimation on embedded devices with highly constrained memory
and compute envelope, such as AR/VR wearables. Our 2.5D hand pose estimation
consists of 2D key-point estimation of joint positions on an egocentric image,
captured by a depth sensor, and lifted to 2.5D using the corresponding depth
values. Our contributions are two fold: (a) We discuss data labeling and
augmentation strategies, the modules in the network architecture that
collectively lead to $3\%$ the flop count and $2\%$ the number of parameters
when compared to the state of the art MobileNetV2 architecture. (b) We propose
an auxiliary multi-task training strategy needed to compensate for the small
capacity of the network while achieving comparable performance to MobileNetV2.
Our 32-bit trained model has a memory footprint of less than 300 Kilobytes,
operates at more than 50 Hz with less than 35 MFLOPs.

本研究介绍了在嵌入式设备上部署高效率 2.5D 手部姿势估计所需的数据、架构和训练过程，提出了标签和增强策略，并综合多模块网络构件和辅助多任务训练策略实现了与 MobileNetV2 相当性能的模型。该模型具有小的内存占用和高效的计算能力。