Project import generated by Copybara.

GitOrigin-RevId: bbbbcb4f5174dea33525729ede47c770069157cd

docs/solutions/hands.md

@@ -120,7 +120,7 @@ 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.tflite)
+[model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/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
@@ -163,6 +163,11 @@ unrelated, images. Default to `false`.
 
 Maximum number of hands to detect. Default to `2`.
 
+#### model_complexity
+
+Complexity of the hand landmark model: `0` or `1`. Landmark accuracy as well as
+inference latency generally go up with the model complexity. Default to `1`.
+
 #### min_detection_confidence
 
 Minimum confidence value (`[0.0, 1.0]`) from the hand detection model for the
@@ -212,6 +217,7 @@ Supported configuration options:
 
 *   [static_image_mode](#static_image_mode)
 *   [max_num_hands](#max_num_hands)
+*   [model_complexity](#model_complexity)
 *   [min_detection_confidence](#min_detection_confidence)
 *   [min_tracking_confidence](#min_tracking_confidence)
 
@@ -260,6 +266,7 @@ with mp_hands.Hands(
 # For webcam input:
 cap = cv2.VideoCapture(0)
 with mp_hands.Hands(
+    model_complexity=0,
     min_detection_confidence=0.5,
     min_tracking_confidence=0.5) as hands:
   while cap.isOpened():
@@ -302,6 +309,7 @@ and a [fun application], and the following usage example.
 Supported configuration options:
 
 *   [maxNumHands](#max_num_hands)
+*   [modelComplexity](#model_complexity)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
 
@@ -351,6 +359,7 @@ const hands = new Hands({locateFile: (file) => {
 }});
 hands.setOptions({
   maxNumHands: 2,
+  modelComplexity: 1,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5
 });

docs/solutions/models.md

@@ -58,10 +58,12 @@ one over the other.
     [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection.tflite),
     [TF.js model](https://tfhub.dev/mediapipe/handdetector/1)
 *   Hand landmark model:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark.tflite),
+    [TFLite model (lite)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite),
+    [TFLite model (full)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_full.tflite),
     [TFLite model (sparse)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_sparse.tflite),
     [TF.js model](https://tfhub.dev/mediapipe/handskeleton/1)
-*   [Model card](https://mediapipe.page.link/handmc), [Model card (sparse)](https://mediapipe.page.link/handmc-sparse)
+*   [Model card](https://mediapipe.page.link/handmc),
+    [Model card (sparse)](https://mediapipe.page.link/handmc-sparse)
 
 ### [Pose](https://google.github.io/mediapipe/solutions/pose)
 

docs/solutions/pose.md

@@ -125,7 +125,7 @@ hip midpoints.
 :----------------------------------------------------------------------------------------------------: |
 *Fig 3. Vitruvian man aligned via two virtual keypoints predicted by BlazePose detector in addition to the face bounding box.* |
 
-### Pose Landmark Model (BlazePose GHUM 3D)
+### Pose Landmark Model (BlazePose [GHUM](https://github.com/google-research/google-research/tree/master/ghum) 3D)
 
 The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks
 (see figure below).
@@ -486,6 +486,7 @@ on how to build MediaPipe examples.
     [BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
     ([presentation](https://youtu.be/YPpUOTRn5tA))
 *   [Models and model cards](./models.md#pose)
+*   [GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models](https://github.com/google-research/google-research/tree/master/ghum)
 *   [Web demo](https://code.mediapipe.dev/codepen/pose)
 *   [Python Colab](https://mediapipe.page.link/pose_py_colab)