论文标题
MediaPipe Hands：启用实时手动跟踪
MediaPipe Hands: On-device Real-time Hand Tracking
论文作者
论文摘要
我们提出了一个实时的机上跟踪管道，该管道可预测用于AR/VR应用的单RGB摄像机的手骨架。管道由两个模型组成：1）棕榈探测器，2）手动地标模型。它是通过MediaPipe实施的，MediaPipe是构建跨平台ML解决方案的框架。提出的模型和管道体系结构表明了移动GPU和高预测质量的实时推理速度。 MediaPipe Hands在https://mediapipe.dev上开源。
We present a real-time on-device hand tracking pipeline that predicts hand skeleton from single RGB camera for AR/VR applications. The pipeline consists of two models: 1) a palm detector, 2) a hand landmark model. It's implemented via MediaPipe, a framework for building cross-platform ML solutions. The proposed model and pipeline architecture demonstrates real-time inference speed on mobile GPUs and high prediction quality. MediaPipe Hands is open sourced at https://mediapipe.dev.
