The MediaPipe Holistic Landmarker task lets you combine components of the face, hand, and pose landmarkers to detect a total of 543 human body landmarks in real-time. You can use this task to analyze full-body gestures, poses, and actions across a continuous stream of images or video.
The task outputs a combination of normalized landmarks, world landmarks, blendshapes (optional), and segmentation masks (optional).
Get Started
Start using this task by following one of the implementation guides for your target platform:
- Android - Code example - Guide
- iOS - Code example - Guide
- Python - Code example - Guide
- Web - Code example - Guide
Task details
This section describes the capabilities, inputs, outputs, and configuration options of this task.
Features
- Input image processing - Processing includes image rotation, resizing, normalization, and color space conversion.
- Score thresholds - Filter results based on prediction and confidence scores.
- Optional outputs - Optional output of face blendshapes and pose segmentation masks.
| Task inputs | Task outputs |
|---|---|
The Holistic Landmarker accepts an input of one of the following data
types:
|
The Holistic Landmarker outputs the following results:
|
Configuration options
This task has the following configuration options:
| Option Name | Description | Value Range | Default Value |
|---|---|---|---|
running_mode |
Sets the running mode for the task. There are three modes: IMAGE: The mode for single image inputs. VIDEO: The mode for decoded frames of a video. LIVE_STREAM: The mode for a livestream of input data, such as from a camera. |
{IMAGE, VIDEO, LIVE_STREAM} |
IMAGE |
min_face_detection_confidence |
The minimum confidence score for face detection to be considered successful. | Float [0.0, 1.0] |
0.5 |
min_face_suppression_threshold |
The minimum non-maximum-suppression threshold for face detection to be considered overlapped. | Float [0.0, 1.0] |
0.3 |
min_face_presence_confidence |
The minimum confidence score of face presence score in the face landmark detection. | Float [0.0, 1.0] |
0.5 |
min_pose_detection_confidence |
The minimum confidence score for pose detection to be considered successful. | Float [0.0, 1.0] |
0.5 |
min_pose_suppression_threshold |
The minimum threshold for pose suppression score in the pose detection. | Float [0.0, 1.0] |
0.3 |
min_pose_presence_confidence |
The minimum confidence score of pose presence score in the pose landmark detection. | Float [0.0, 1.0] |
0.5 |
min_hand_landmarks_confidence |
The minimum confidence score of hand presence score in the hand landmarks detection. | Float [0.0, 1.0] |
0.5 |
output_face_blendshapes |
Whether to output face blendshapes classification, which can be used to animate a 3D model. | Boolean |
false |
output_pose_segmentation_masks |
Whether to output segmentation masks for the human pose. | Boolean |
false |
Models
The Holistic Landmarker uses a series of packaged models to perform holistic landmarks detection. These models include face detection, face mesh/landmarks, pose detection, pose landmarker, palm detection, and hand landmark models.
The following models are packaged together into a downloadable model bundle:
- Pose detection and landmark model: tracks 33 body pose coordinates.
- Face detection and mesh model: detects and tracks 468 3D face mesh landmarks, with optional 52 blendshape coefficients.
- Palm detection and hand landmark model: tracks 21 knuckle coordinates per hand.
| Model bundle | Data type | Model Cards | Versions |
|---|---|---|---|
| Holistic landmarker | float 16 |
BlazePose FaceMesh-V2 HandLandmarker |
Latest |
Landmark Coordinates
The Holistic Landmarker outputs a total of 543 landmarks that represent the full body: - Pose landmarks: 33 landmarks representing key body coordinate points (same as the Pose Landmarker). - Face landmarks: 468 landmarks representing the facial structure (same as the Face Landmarker). - Hand landmarks: 21 landmarks per hand (42 total) representing knuckle coordinates (same as the Hand Landmarker).