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Mouwrice · May 6, 2024 · 0e3c071 · 0e3c071
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diff --git a/mediapipe_pose.typ b/mediapipe_pose.typ
@@ -18,11 +18,10 @@ One other feature other than returning the body pose is the creation of an image
 
 MediaPipe Pose is based on two models for the inference of the body pose. The first one is BlazePose which is only designed to return two-dimensional data in the given frame @blazepose. A second is the GHUM model, a model that captures 3D meshes given human body scans. A synthetic depth is obtained via the GHUM model fitted to the 2D points @ghum.
 
-The Pose task consists of 3 `.task` files that contain the actual detection task and models. Three models are available: Lite (3MB size), Full (6MB size) and Heavy (26MB size).
+The Pose task consists of 3 `.task` files that contain the actual detection task and models. Three models are available: Lite (3 MB size), Full (6 MB size) and Heavy (26 MB size).
 The measurement results will show that the larger models are able to provide a more accurate and correct result but at reduced inference speeds. There is a trade-off to be made between accuracy and real-time processing. Although the Heavy model is almost 9 times larger than the Lite model, the improvement in accuracy is a lot smaller. The Lite model is not considerably worse and produces fine results for the drumming application.
 
 
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 ==== BlazePose
 
 BlazePose provides human pose tracking by employing machine learning (ML) to infer 33, 2D landmarks of a body from a single frame @blazepose. A standard for body pose originating from 2020 is the COCO topology @coco. The COCO 2020 Keypoint Detection Task is a challenge to develop a solution to accurately detect and locate the keypoints from the COCO dataset. The original COCO topology only consists of 17 landmarks. With little to no landmarks on the hands and feet. BlazePose extends this topology to a total of 33 landmarks by providing landmarks for the hands and feet as well. These added landmarks are crucial for the drumming application and is part of the reason MediaPipe was chosen.
@@ -58,5 +57,3 @@ The model takes an image as input with a fixed size of 256 by 256 pixels and 3 v
 ==== GHUM (Generative 3D Human Shape and Articulated Pose Models)
 
 As mentioned, MediaPipe Pose offers an extra dimension unlike many other purely 2D pose estimation tools. This third dimension is of course depth. It does so by utilising an entirely different model being the GHUM model @ghum. The GHUM model is able to construct a full 3D body mesh given image scans of a person. The outputs form an actual 3D mesh of 10,168 vertices for the regular model and 3,194 vertices  for the lite model. MediaPipe is quite vague on the exact usage of this model in the pose solution. The only mention of GHUM is in the following sentence: _"Keypoint Z-value estimate is provided using synthetic data, obtained via the GHUM model (articulated 3D human shape model) fitted to 2D point projections."_ @mediapipe-model-card. Nonetheless, in the measurements it is shown that there is some notion of depth, but it is far from accurate. Especially when compared to the other 2D values provided by the BlazePose model.
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