+ Bare Metal Performance. The Hand Pose predictor is implemented with NatML, which takes advantage of hardware machine learning accelerators, like CoreML on iOS and macOS, NNAPI on Android, and DirectML on Windows.
+ Extremely Easy to Use. The Hand Pose predictor accepts an image (Texture2D, WebCamTexture, NatDevice, and more) and returns a hand pose with . As such, it can be used in only a few lines of code. See more on NatML Hub.
+ Cross Platform. The Hand Pose predictor supports Android, iOS, macOS, and Windows alike. As such you can test it in the Editor, and deploy it to the device all in one seamless workflow.
+ Augmented Reality. This predictor is particularly suited for augmented reality applications, where it can create hand gesture recognition experiences for users.
+ Lightweight Package. This package contains the predictor scripts, whereas the ML model will be downloaded at runtime from NatML Hub and cached onto the device, reducing the app size significantly.
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