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GitOrigin-RevId: 1e13be30e2c6838d4a2ff768a39c414bc80534bb
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MediaPipe Team
2022-09-06 21:46:17 +00:00
committed by Sebastian Schmidt
parent 63e679d99c
commit 4dc4b19ddb
639 changed files with 71327 additions and 2078 deletions
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docs/solutions/hands.md
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@@ -38,7 +38,7 @@ hand perception functionality to the wider research and development community
will result in an emergence of creative use cases, stimulating new applications
and new research avenues.
![hand_tracking_3d_android_gpu.gif](../images/mobile/hand_tracking_3d_android_gpu.gif) |
![hand_tracking_3d_android_gpu.gif](https://mediapipe.dev/images/mobile/hand_tracking_3d_android_gpu.gif) |
:------------------------------------------------------------------------------------: |
*Fig 1. Tracked 3D hand landmarks are represented by dots in different shades, with the brighter ones denoting landmarks closer to the camera.* |
@@ -91,9 +91,9 @@ To detect initial hand locations, we designed a
mobile real-time uses in a manner similar to the face detection model in
[MediaPipe Face Mesh](./face_mesh.md). Detecting hands is a decidedly complex
task: our
[lite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_lite.tflite)
[lite model](https://storage.googleapis.com/mediapipe-assets/palm_detection_lite.tflite)
and
[full model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_full.tflite)
[full model](https://storage.googleapis.com/mediapipe-assets/palm_detection_full.tflite)
have to work across a variety of hand sizes with a large scale span (~20x)
relative to the image frame and be able to detect occluded and self-occluded
hands. Whereas faces have high contrast patterns, e.g., in the eye and mouth
@@ -122,7 +122,7 @@ just 86.22%.
### Hand Landmark Model
After the palm detection over the whole image our subsequent hand landmark
[model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_full.tflite)
[model](https://storage.googleapis.com/mediapipe-assets/hand_landmark_full.tflite)
performs precise keypoint localization of 21 3D hand-knuckle coordinates inside
the detected hand regions via regression, that is direct coordinate prediction.
The model learns a consistent internal hand pose representation and is robust
@@ -135,11 +135,11 @@ and provide additional supervision on the nature of hand geometry, we also
render a high-quality synthetic hand model over various backgrounds and map it
to the corresponding 3D coordinates.
![hand_landmarks.png](../images/mobile/hand_landmarks.png) |
![hand_landmarks.png](https://mediapipe.dev/images/mobile/hand_landmarks.png) |
:--------------------------------------------------------: |
*Fig 2. 21 hand landmarks.* |
![hand_crops.png](../images/mobile/hand_crops.png) |
![hand_crops.png](https://mediapipe.dev/images/mobile/hand_crops.png) |
:-------------------------------------------------------------------------: |
*Fig 3. Top: Aligned hand crops passed to the tracking network with ground truth annotation. Bottom: Rendered synthetic hand images with ground truth annotation.* |