![]() ![]() Pose landmarker (Full) Pose detector: 224 x 224 x 3 Pose landmarker (lite) Pose detector: 224 x 224 x 3 Attention: This MediaPipe Solutions Preview is an early release. This variant of theĪ 3D human shape modeling pipeline, to estimate the full 3D body pose of an This bundle uses a convolutional neural networkįor on-device, real-time fitness applications. Outputs an estimate of 33 3-dimensional pose landmarks. Pose landmarker model: adds a complete mapping of the pose.Pose detection model: detects the presence of bodies with a few key pose.The following models are packaged together into a downloadable model bundle: Model detects the presence of human bodies within an image frame, and the second The Pose Landmarker uses a series of models to predict pose landmarks. Sets the result listener to receive the landmarker resultsĪsynchronously when Pose Landmarker is in the live stream mode.Ĭan only be used when running mode is set to LIVE_STREAM Whether Pose Landmarker outputs a segmentation mask for the detected The minimum confidence score for the pose tracking The minimum confidence score of pose presence The minimum confidence score for the pose detection to be The maximum number of poses that can be detected by the In this mode, you mustĬall the result_callback listener to receive the Live stream of input data, such as from camera. LIVE_STREAM: The mode for recognizing pose landmarks on a.VIDEO: The mode for recognizing pose landmarks on the.IMAGE: The mode for recognizing pose landmarks on.This task has the following configuration options: Option Name Optional: a segmentation mask for the pose.Pose landmarks in normalized image coordinates.The Pose Landmarker outputs the following results: The Pose Landmarker accepts an input of one of the following data types: ![]()
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