The MediaPipe Holistic Landmarker task lets you combine components of the face, hand, and pose landmarkers to detect a total of 553 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). [Open in Web Demos](https://google-ai-edge.github.io/mediapipe-samples-web/#/vision/holistic_landmarker)[Edit in StackBlitz](https://stackblitz.com/github/google-ai-edge/mediapipe-samples-web?file=src/tasks/holistic-landmarker.ts)
## Quickstart Install the SDK for your platform, [prepare input data](https://developers.google.com/edge/mediapipe/solutions/prepare_data.md.txt#vision), choose a running mode, and run inference. ### Web npm`npm install @mediapipe/tasks-vision`[Web demo](https://google-ai-edge.github.io/mediapipe-samples-web/#/vision/holistic_landmarker)·[Edit in StackBlitz](https://stackblitz.com/github/google-ai-edge/mediapipe-samples-web?file=src/tasks/holistic-landmarker.ts)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_web.md.txt) #### Image `IMAGE`Stateless · single frameSynchronous call on independent still images. Resets temporal tracking state on every call. ```typescript import { FilesetResolver, HolisticLandmarker } from "@mediapipe/tasks-vision"; const vision = await FilesetResolver.forVisionTasks( "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm" ); const holisticLandmarker = await HolisticLandmarker.createFromOptions(vision, { baseOptions: { modelAssetPath: "holistic_landmarker.task", delegate: "GPU" }, runningMode: "IMAGE", minFaceDetectionConfidence: 0.5, minPoseDetectionConfidence: 0.5, }); // Synchronous single-frame call (stateless across images) const image = document.getElementById("image") as HTMLImageElement; const result = holisticLandmarker.detect(image); ``` #### Video `VIDEO`Every frame · no dropsSynchronous tracking across decoded video frames with monotonic timestamps. Processes every frame in order and never drops frames. ```typescript import { FilesetResolver, HolisticLandmarker } from "@mediapipe/tasks-vision"; const vision = await FilesetResolver.forVisionTasks( "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm" ); const holisticLandmarker = await HolisticLandmarker.createFromOptions(vision, { baseOptions: { modelAssetPath: "holistic_landmarker.task", delegate: "GPU" }, runningMode: "VIDEO", minFaceDetectionConfidence: 0.5, minPoseDetectionConfidence: 0.5, }); // Synchronous across decoded frames with monotonic timestamps (never drops frames) for (const { frame, timestampMs } of decodedVideoFrames) { const result = holisticLandmarker.detectForVideo(frame, timestampMs); } ``` #### Livestream `VIDEO + rAF`Live camera · latest frameWeb tasks run synchronously---pair `runningMode: "VIDEO"` with `requestAnimationFrame` so each paint inspects only the latest camera frame (skipping intermediate frames when busy). ```typescript import { FilesetResolver, HolisticLandmarker } from "@mediapipe/tasks-vision"; const vision = await FilesetResolver.forVisionTasks( "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm" ); // On Web, live camera loops pair runningMode: "VIDEO" with requestAnimationFrame const holisticLandmarker = await HolisticLandmarker.createFromOptions(vision, { baseOptions: { modelAssetPath: "holistic_landmarker.task", delegate: "GPU" }, runningMode: "VIDEO", minFaceDetectionConfidence: 0.5, minPoseDetectionConfidence: 0.5, }); const video = document.getElementById("webcam") as HTMLVideoElement; video.srcObject = await navigator.mediaDevices.getUserMedia({ video: true }); await video.play(); let lastVideoTime = -1; function renderLoop() { if (video.readyState >= 2 && video.currentTime !== lastVideoTime) { lastVideoTime = video.currentTime; const result = holisticLandmarker.detectForVideo(video, performance.now()); console.log(result.poseLandmarks, result.faceLandmarks); } requestAnimationFrame(renderLoop); } renderLoop(); ``` ### Android (Kotlin) Gradle`implementation("com.google.mediapipe:tasks-vision:latest.release")`[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/holistic_landmarker/android)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_android.md.txt) #### Image `IMAGE`Stateless · single frameSynchronous call on independent still images. Resets temporal tracking state on every call. ```kotlin import com.google.mediapipe.framework.image.BitmapImageBuilder import com.google.mediapipe.framework.image.MPImage import com.google.mediapipe.tasks.core.BaseOptions import com.google.mediapipe.tasks.vision.core.RunningMode import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarker val baseOptions = BaseOptions.builder() .setModelAssetPath("holistic_landmarker.task") .build() val options = HolisticLandmarker.HolisticLandmarkerOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.IMAGE) .setMinFaceDetectionConfidence(0.5f) .setMinPoseDetectionConfidence(0.5f) .build() val holisticLandmarker = HolisticLandmarker.createFromOptions(context, options) // Synchronous single-image call (no temporal tracking across calls) val mpImage = BitmapImageBuilder(bitmap).build() val result = holisticLandmarker.detect(mpImage) ``` #### Video `VIDEO`Every frame · no dropsSynchronous tracking across decoded video frames with monotonic timestamps. Processes every frame in order and never drops frames. ```kotlin import com.google.mediapipe.framework.image.BitmapImageBuilder import com.google.mediapipe.framework.image.MPImage import com.google.mediapipe.tasks.core.BaseOptions import com.google.mediapipe.tasks.vision.core.RunningMode import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarker val baseOptions = BaseOptions.builder() .setModelAssetPath("holistic_landmarker.task") .build() val options = HolisticLandmarker.HolisticLandmarkerOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.VIDEO) .setMinFaceDetectionConfidence(0.5f) .setMinPoseDetectionConfidence(0.5f) .build() val holisticLandmarker = HolisticLandmarker.createFromOptions(context, options) // Synchronous: processes every frame in order with monotonic timestamps (no frame drops) val mpImage = BitmapImageBuilder(frameBitmap).build() val result = holisticLandmarker.detectForVideo(mpImage, frameTimestampMs) ``` #### Livestream `LIVE_STREAM`Async · drops frames when busyAsynchronous camera pipeline with a result callback. Automatically drops incoming frames when busy so results stay current with low latency. ```kotlin import android.os.SystemClock import android.util.Log import com.google.mediapipe.framework.image.BitmapImageBuilder import com.google.mediapipe.framework.image.MPImage import com.google.mediapipe.tasks.core.BaseOptions import com.google.mediapipe.tasks.vision.core.RunningMode import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarker val baseOptions = BaseOptions.builder() .setModelAssetPath("holistic_landmarker.task") .build() val options = HolisticLandmarker.HolisticLandmarkerOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.LIVE_STREAM) .setMinFaceDetectionConfidence(0.5f) .setMinPoseDetectionConfidence(0.5f) .setResultListener { result, _ -> val pose = result.poseLandmarks() } .setErrorListener { error -> Log.e("MediaPipe", error.message.orEmpty()) } .build() val holisticLandmarker = HolisticLandmarker.createFromOptions(context, options) // Async: automatically drops incoming camera frames when busy so results stay current holisticLandmarker.detectAsync(mpImage, SystemClock.uptimeMillis()) ``` ### Android (Java) Gradle`implementation 'com.google.mediapipe:tasks-vision:latest.release'`[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/holistic_landmarker/android)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_android.md.txt) #### Image `IMAGE`Stateless · single frameSynchronous call on independent still images. Resets temporal tracking state on every call. ```java import com.google.mediapipe.framework.image.BitmapImageBuilder; import com.google.mediapipe.framework.image.MPImage; import com.google.mediapipe.tasks.core.BaseOptions; import com.google.mediapipe.tasks.vision.core.RunningMode; import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarker; import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarkerResult; BaseOptions baseOptions = BaseOptions.builder() .setModelAssetPath("holistic_landmarker.task") .build(); HolisticLandmarker.HolisticLandmarkerOptions options = HolisticLandmarker.HolisticLandmarkerOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.IMAGE) .setMinFaceDetectionConfidence(0.5f) .setMinPoseDetectionConfidence(0.5f) .build(); HolisticLandmarker holisticLandmarker = HolisticLandmarker.createFromOptions(context, options); // Synchronous single-image call (no temporal tracking across calls) MPImage mpImage = new BitmapImageBuilder(bitmap).build(); HolisticLandmarkerResult result = holisticLandmarker.detect(mpImage); ``` #### Video `VIDEO`Every frame · no dropsSynchronous tracking across decoded video frames with monotonic timestamps. Processes every frame in order and never drops frames. ```java import com.google.mediapipe.framework.image.BitmapImageBuilder; import com.google.mediapipe.framework.image.MPImage; import com.google.mediapipe.tasks.core.BaseOptions; import com.google.mediapipe.tasks.vision.core.RunningMode; import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarker; import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarkerResult; BaseOptions baseOptions = BaseOptions.builder() .setModelAssetPath("holistic_landmarker.task") .build(); HolisticLandmarker.HolisticLandmarkerOptions options = HolisticLandmarker.HolisticLandmarkerOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.VIDEO) .setMinFaceDetectionConfidence(0.5f) .setMinPoseDetectionConfidence(0.5f) .build(); HolisticLandmarker holisticLandmarker = HolisticLandmarker.createFromOptions(context, options); // Synchronous: processes every frame in order with monotonic timestamps (no frame drops) MPImage mpImage = new BitmapImageBuilder(frameBitmap).build(); HolisticLandmarkerResult result = holisticLandmarker.detectForVideo(mpImage, frameTimestampMs); ``` #### Livestream `LIVE_STREAM`Async · drops frames when busyAsynchronous camera pipeline with a result callback. Automatically drops incoming frames when busy so results stay current with low latency. ```java import android.os.SystemClock; import android.util.Log; import com.google.mediapipe.framework.image.BitmapImageBuilder; import com.google.mediapipe.framework.image.MPImage; import com.google.mediapipe.tasks.core.BaseOptions; import com.google.mediapipe.tasks.vision.core.RunningMode; import com.google.mediapipe.tasks.vision.holisticlandmarker.HolisticLandmarker; BaseOptions baseOptions = BaseOptions.builder() .setModelAssetPath("holistic_landmarker.task") .build(); HolisticLandmarker.HolisticLandmarkerOptions options = HolisticLandmarker.HolisticLandmarkerOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.LIVE_STREAM) .setMinFaceDetectionConfidence(0.5f) .setMinPoseDetectionConfidence(0.5f) .setResultListener((result, inputImage) -> { var pose = result.poseLandmarks(); }) .setErrorListener(error -> Log.e("MediaPipe", String.valueOf(error.getMessage()))) .build(); HolisticLandmarker holisticLandmarker = HolisticLandmarker.createFromOptions(context, options); // Async: automatically drops incoming camera frames when busy so results stay current holisticLandmarker.detectAsync(mpImage, SystemClock.uptimeMillis()); ``` ### iOS SwiftPM`https://github.com/google-ai-edge/mediapipe → MediaPipeTasksVision`[Package.swift](https://developers.google.com/edge/mediapipe/solutions/setup_ios.md.txt#package_swift)·[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/holistic_landmarker/ios)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_ios.md.txt) #### Image `IMAGE`Stateless · single frameSynchronous call on independent still images. Resets temporal tracking state on every call. ```swift import Foundation import MediaPipeTasksVision guard let modelPath = Bundle.main.path(forResource: "holistic_landmarker", ofType: "task") else { return } let options = HolisticLandmarkerOptions() options.baseOptions.modelAssetPath = modelPath options.runningMode = .image options.minFaceDetectionConfidence = 0.5 options.minPoseDetectionConfidence = 0.5 let holisticLandmarker = try HolisticLandmarker(options: options) // Synchronous single-image call (stateless across images) let mpImage = try MPImage(uiImage: image) let result = try holisticLandmarker.detect(image: mpImage) ``` #### Video `VIDEO`Every frame · no dropsSynchronous tracking across decoded video frames with monotonic timestamps. Processes every frame in order and never drops frames. ```swift import Foundation import MediaPipeTasksVision guard let modelPath = Bundle.main.path(forResource: "holistic_landmarker", ofType: "task") else { return } let options = HolisticLandmarkerOptions() options.baseOptions.modelAssetPath = modelPath options.runningMode = .video options.minFaceDetectionConfidence = 0.5 options.minPoseDetectionConfidence = 0.5 let holisticLandmarker = try HolisticLandmarker(options: options) // Synchronous: processes every frame in order with monotonic timestamps (no frame drops) let mpImage = try MPImage(pixelBuffer: pixelBuffer) let result = try holisticLandmarker.detect(videoFrame: mpImage, timestampInMilliseconds: timestampMs) ``` #### Livestream `LIVE_STREAM`Async · drops frames when busyAsynchronous camera pipeline with a result callback. Automatically drops incoming frames when busy so results stay current with low latency. ```swift import Foundation import MediaPipeTasksVision final class HolisticLandmarkerStreamService: NSObject { private var holisticLandmarker: HolisticLandmarker? func setUp() throws { guard let modelPath = Bundle.main.path( forResource: "holistic_landmarker", ofType: "task" ) else { return } let options = HolisticLandmarkerOptions() options.baseOptions.modelAssetPath = modelPath options.runningMode = .liveStream options.minFaceDetectionConfidence = 0.5 options.minPoseDetectionConfidence = 0.5 options.holisticLandmarkerLiveStreamDelegate = self holisticLandmarker = try HolisticLandmarker(options: options) } func detectAsync(mpImage: MPImage, timestampMs: Int) throws { // Async: automatically drops incoming camera frames when busy so results stay current try holisticLandmarker?.detectAsync(image: mpImage, timestampInMilliseconds: timestampMs) } } extension HolisticLandmarkerStreamService: HolisticLandmarkerLiveStreamDelegate { func holisticLandmarker( _ holisticLandmarker: HolisticLandmarker, didFinishDetection result: HolisticLandmarkerResult?, timestampInMilliseconds: Int, error: (any Error)? ) { guard let result else { return } DispatchQueue.main.async { let pose = result.poseLandmarks } } } ``` ### Python pip`pip install mediapipe`[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/holistic_landmarker/python)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_python.md.txt) #### Image `IMAGE`Stateless · single frameSynchronous call on independent still images. Resets temporal tracking state on every call. ```python import mediapipe as mp BaseOptions = mp.tasks.BaseOptions HolisticLandmarker = mp.tasks.vision.HolisticLandmarker HolisticLandmarkerOptions = mp.tasks.vision.HolisticLandmarkerOptions VisionRunningMode = mp.tasks.vision.RunningMode options = HolisticLandmarkerOptions( base_options=BaseOptions(model_asset_path="holistic_landmarker.task"), running_mode=VisionRunningMode.IMAGE, min_face_detection_confidence=0.5, min_pose_detection_confidence=0.5, ) with HolisticLandmarker.create_from_options(options) as landmarker: # Synchronous single-image call (stateless across images) mp_image = mp.Image.create_from_file("input.jpg") result = landmarker.detect(mp_image) ``` #### Video `VIDEO`Every frame · no dropsSynchronous tracking across decoded video frames with monotonic timestamps. Processes every frame in order and never drops frames. ```python import mediapipe as mp BaseOptions = mp.tasks.BaseOptions HolisticLandmarker = mp.tasks.vision.HolisticLandmarker HolisticLandmarkerOptions = mp.tasks.vision.HolisticLandmarkerOptions VisionRunningMode = mp.tasks.vision.RunningMode options = HolisticLandmarkerOptions( base_options=BaseOptions(model_asset_path="holistic_landmarker.task"), running_mode=VisionRunningMode.VIDEO, min_face_detection_confidence=0.5, min_pose_detection_confidence=0.5, ) with HolisticLandmarker.create_from_options(options) as landmarker: # Synchronous: processes every frame in order with monotonic timestamps (no frame drops) for mp_image, timestamp_ms in decoded_video_frames: result = landmarker.detect_for_video(mp_image, timestamp_ms) ``` #### Livestream `LIVE_STREAM`Async · drops frames when busyAsynchronous camera pipeline with a result callback. Automatically drops incoming frames when busy so results stay current with low latency. ```python import mediapipe as mp BaseOptions = mp.tasks.BaseOptions HolisticLandmarker = mp.tasks.vision.HolisticLandmarker HolisticLandmarkerOptions = mp.tasks.vision.HolisticLandmarkerOptions VisionRunningMode = mp.tasks.vision.RunningMode def on_result(result, output_image: mp.Image, timestamp_ms: int): print(f"Pose landmarks at {timestamp_ms} ms: {len(result.pose_landmarks)}") options = HolisticLandmarkerOptions( base_options=BaseOptions(model_asset_path="holistic_landmarker.task"), running_mode=VisionRunningMode.LIVE_STREAM, min_face_detection_confidence=0.5, min_pose_detection_confidence=0.5, result_callback=on_result, ) with HolisticLandmarker.create_from_options(options) as landmarker: # Async: automatically drops incoming camera frames when busy so results stay current mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame) landmarker.detect_async(mp_image, timestamp_ms) ``` ### Flutter pub`flutter pub add mediapipe_vision`[Live gallery](https://google.github.io/flutter-mediapipe/)·[Sample code](https://github.com/google/flutter-mediapipe/tree/main/samples/vision/holistic_landmarker)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_flutter.md.txt) #### Image `RunningMode.image`Stateless · single frameOne call per independent still image. No tracking state carries over between calls. ```dart import 'package:mediapipe_vision/mediapipe_vision.dart'; Future trackPerson(VisionImage image) async { final landmarker = await HolisticLandmarker.create( HolisticLandmarkerOptions(model: VisionModels.holisticLandmarker), ); try { final result = await landmarker.detect(image); print('Face: ${result.faceLandmarks.length} landmarks'); print('Pose: ${result.poseLandmarks.length} landmarks'); print('Left hand: ${result.leftHandLandmarks.length} landmarks'); print('Right hand: ${result.rightHandLandmarks.length} landmarks'); } finally { await landmarker.dispose(); } } ``` #### Video `RunningMode.video`Every frame · no dropsTracking across decoded video frames with strictly increasing timestamps. Processes every frame in order and never drops frames. ```dart import 'dart:typed_data'; import 'package:mediapipe_vision/mediapipe_vision.dart'; typedef Frame = ({Uint8List rgba, int width, int height, int milliseconds}); Future trackPersonInVideo(Stream frames) async { final landmarker = await HolisticLandmarker.create( HolisticLandmarkerOptions( model: VisionModels.holisticLandmarker, runningMode: RunningMode.video, ), ); try { await for (final frame in frames) { final result = await landmarker.detectForVideo( VisionImage.fromPixels( pixels: frame.rgba, width: frame.width, height: frame.height, format: VisionPixelFormat.rgba, ), timestampMilliseconds: frame.milliseconds, ); final ms = result.timestampMilliseconds; print( '$ms ms: ${result.faceLandmarks.length} face, ' '${result.poseLandmarks.length} pose, ' '${result.leftHandLandmarks.length} left hand and ' '${result.rightHandLandmarks.length} right hand landmarks', ); } } finally { await landmarker.dispose(); } } ``` #### Livestream `RunningMode.liveStream`Async · drops frames when busyEach call returns at once and results arrive on the task's `results` stream. One frame runs at a time; a newer frame replaces one that's waiting, so results stay current. ```dart import 'dart:typed_data'; import 'package:mediapipe_vision/mediapipe_vision.dart'; typedef Frame = ({Uint8List rgba, int width, int height, int milliseconds}); Future trackPersonLive(Stream frames) async { final landmarker = await HolisticLandmarker.create( HolisticLandmarkerOptions( model: VisionModels.holisticLandmarker, runningMode: RunningMode.liveStream, ), ); // Listen before the first frame, as Google requires a result listener. final results = landmarker.results.listen((result) { final ms = result.timestampMilliseconds; print( '$ms ms: ${result.faceLandmarks.length} face, ' '${result.poseLandmarks.length} pose, ' '${result.leftHandLandmarks.length} left hand and ' '${result.rightHandLandmarks.length} right hand landmarks', ); }); try { await for (final frame in frames) { landmarker.detectAsync( VisionImage.fromPixels( pixels: frame.rgba, width: frame.width, height: frame.height, format: VisionPixelFormat.rgba, ), timestampMilliseconds: frame.milliseconds, ); } } finally { // Runs the frame in flight and the waiting one, then closes results. await landmarker.dispose(); await results.cancel(); print('${landmarker.droppedFrames} frames dropped'); } } ``` On Web, you can use `DrawingUtils(ctx).drawConnectors(...)` with `PoseLandmarker.POSE_CONNECTIONS`, `HandLandmarker.HAND_CONNECTIONS`, and `FaceLandmarker.FACE_LANDMARKS_TESSELATION` from `@mediapipe/tasks-vision` to render holistic overlays on an HTML ``. ## 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. Inputs The Holistic Landmarker accepts an input of one of the following data types: - Still images - Decoded video frames - Live video feed See [Prepare vision input data](https://developers.google.com/edge/mediapipe/solutions/prepare_data.md.txt#vision) for `MPImage`, `mp.Image`, `VisionImage`, and browser media examples. Outputs The Holistic Landmarker outputs the following results: - Face landmarks in normalized image coordinates (478 landmarks) - Pose landmarks in normalized image coordinates and 3D world coordinates (33 landmarks) - Left hand landmarks in normalized image coordinates and 3D world coordinates (21 landmarks) - Right hand landmarks in normalized image coordinates and 3D world coordinates (21 landmarks) - Optional: Face blendshapes scores - Optional: A segmentation mask for the pose region ### Configuration options This task has the following configuration options: ### Web | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded video frames and live camera streams. | {`IMAGE`, `VIDEO`} | `IMAGE` | | `minFaceDetectionConfidence` | The minimum confidence score for face detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minFaceSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping face detections. | Float `[0.0, 1.0]` | `0.3` | | `minFacePresenceConfidence` | The minimum confidence score for face presence in face landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minPoseDetectionConfidence` | The minimum confidence score for pose detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minPoseSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping pose detections. | Float `[0.0, 1.0]` | `0.3` | | `minPosePresenceConfidence` | The minimum confidence score for pose presence in pose landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minHandLandmarksConfidence` | The minimum confidence score for hand presence in hand landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `outputFaceBlendshapes` | Whether to output face blendshape classifications for 3D facial expression rendering. | `Boolean` | `false` | | `outputPoseSegmentationMasks` | Whether to output segmentation masks for the detected pose. | `Boolean` | `false` | ### Android | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded frames of a video. `LIVE_STREAM`: Livestream of camera input data (requires asynchronous listener/delegate). | {`IMAGE`, `VIDEO`, `LIVE_STREAM`} | `IMAGE` | | `minFaceDetectionConfidence` | The minimum confidence score for face detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minFaceSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping face detections. | Float `[0.0, 1.0]` | `0.3` | | `minFacePresenceConfidence` | The minimum confidence score for face presence in face landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minPoseDetectionConfidence` | The minimum confidence score for pose detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minPoseSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping pose detections. | Float `[0.0, 1.0]` | `0.3` | | `minPosePresenceConfidence` | The minimum confidence score for pose presence in pose landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minHandLandmarksConfidence` | The minimum confidence score for hand presence in hand landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `outputFaceBlendshapes` | Whether to output face blendshape classifications for 3D facial expression rendering. | `Boolean` | `false` | | `outputPoseSegmentationMasks` | Whether to output segmentation masks for the detected pose. | `Boolean` | `false` | | `resultListener` | Asynchronous callback for `LIVE_STREAM` mode results. | `OutputHandler.ResultListener` | Not set | | `errorListener` | Optional error listener for `LIVE_STREAM` mode. | `ErrorListener` | Not set | ### iOS | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded frames of a video. `LIVE_STREAM`: Livestream of camera input data (requires asynchronous listener/delegate). | {`IMAGE`, `VIDEO`, `LIVE_STREAM`} | `IMAGE` | | `minFaceDetectionConfidence` | The minimum confidence score for face detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minFaceSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping face detections. | Float `[0.0, 1.0]` | `0.3` | | `minFacePresenceConfidence` | The minimum confidence score for face presence in face landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minPoseDetectionConfidence` | The minimum confidence score for pose detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minPoseSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping pose detections. | Float `[0.0, 1.0]` | `0.3` | | `minPosePresenceConfidence` | The minimum confidence score for pose presence in pose landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minHandLandmarksConfidence` | The minimum confidence score for hand presence in hand landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `outputFaceBlendshapes` | Whether to output face blendshape classifications for 3D facial expression rendering. | `Boolean` | `false` | | `outputPoseSegmentationMasks` | Whether to output segmentation masks for the detected pose. | `Boolean` | `false` | | `holisticLandmarkerLiveStreamDelegate` | Asynchronous delegate for `LIVE_STREAM` mode results (invoked on a serial background queue). | `HolisticLandmarkerLiveStreamDelegate` | Not set | ### Python | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `running_mode` | Sets the running mode for the task: `IMAGE`: Single image inputs. `VIDEO`: Decoded frames of a video. `LIVE_STREAM`: Livestream of camera input data (requires `result_callback`). | {`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 overlapping face detections. | Float `[0.0, 1.0]` | `0.3` | | `min_face_presence_confidence` | The minimum confidence score for face presence in 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 non-maximum-suppression threshold for overlapping pose detections. | Float `[0.0, 1.0]` | `0.3` | | `min_pose_presence_confidence` | The minimum confidence score for pose presence in pose landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `min_hand_landmarks_confidence` | The minimum confidence score for hand presence in hand landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `output_face_blendshapes` | Whether to output face blendshape classifications for 3D facial expression rendering. | `Boolean` | `False` | | `output_pose_segmentation_masks` | Whether to output segmentation masks for the detected pose. | `Boolean` | `False` | | `result_callback` | Asynchronous callback function for `LIVE_STREAM` mode results. | `Callable` | Not set | ### Flutter | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `RunningMode.image`: Single image inputs. `RunningMode.video`: Decoded frames of a video. `RunningMode.liveStream`: Livestream of camera input data (results arrive on the task's `results` stream). | {`RunningMode.image`, `RunningMode.video`, `RunningMode.liveStream`} | `RunningMode.image` | | `minFaceDetectionConfidence` | The minimum confidence score for face detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minFaceSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping face detections. | Float `[0.0, 1.0]` | `0.5` | | `minFacePresenceConfidence` | The minimum confidence score for face presence in face landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minPoseDetectionConfidence` | The minimum confidence score for pose detection to be considered successful. | Float `[0.0, 1.0]` | `0.5` | | `minPoseSuppressionThreshold` | The minimum non-maximum-suppression threshold for overlapping pose detections. | Float `[0.0, 1.0]` | `0.5` | | `minPosePresenceConfidence` | The minimum confidence score for pose presence in pose landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `minHandLandmarksConfidence` | The minimum confidence score for hand presence in hand landmark detection. | Float `[0.0, 1.0]` | `0.5` | | `outputFaceBlendshapes` | Whether to output face blendshape classifications for 3D facial expression rendering. | `bool` | `false` | | `outputPoseSegmentationMask` | Whether to output a segmentation mask for the detected pose. | `bool` | `false` | ### Task output Each `HolisticLandmarkerResult` returns synchronized face, pose, and left/right hand landmarks from a single unified pipeline: | Field | Type / Coordinate Space | Description | |---|---|---| | `faceLandmarks` | `NormalizedLandmark[]` (`x, y` in `[0.0, 1.0]`) | 478 3D facial landmarks normalized by image width and height, with `z` relative to the center of the head (`face_landmarks` in Python). | | `poseLandmarks` | `NormalizedLandmark[]` (`x, y` in `[0.0, 1.0]`) | 33 pose landmarks normalized by image width and height, with `z` relative to the midpoint of the hips (`pose_landmarks` in Python). | | `poseWorldLandmarks` | `Landmark[]` (meters) | 33 3D pose landmarks in real-world meters with the origin at the midpoint of the hips (`pose_world_landmarks` in Python). | | `leftHandLandmarks` | `NormalizedLandmark[]` (`x, y` in `[0.0, 1.0]`) | 21 left-hand 3D landmarks normalized by image width and height (`left_hand_landmarks` in Python). | | `leftHandWorldLandmarks` | `Landmark[]` (meters) | 21 left-hand 3D landmarks in real-world meters (`left_hand_world_landmarks` in Python). | | `rightHandLandmarks` | `NormalizedLandmark[]` (`x, y` in `[0.0, 1.0]`) | 21 right-hand 3D landmarks normalized by image width and height (`right_hand_landmarks` in Python). | | `rightHandWorldLandmarks` | `Landmark[]` (meters) | 21 right-hand 3D landmarks in real-world meters (`right_hand_world_landmarks` in Python). | | `faceBlendshapes` | `Classifications` (`score` in `[0.0, 1.0]`) | Optional 52 facial expression coefficients when `outputFaceBlendshapes` is enabled (`face_blendshapes` in Python; in Dart, a `List?` with no `categories` wrapper, `null` when not requested or no face is found). | | `segmentationMask` | `MPMask` (`Float32` in `[0.0, 1.0]`) | Optional single-channel float pose confidence mask when `outputPoseSegmentationMasks` is enabled (`segmentation_mask` in Python; `poseSegmentationMask` in Dart, a `ConfidenceMask?` enabled with `outputPoseSegmentationMask`). | #### Example `HolisticLandmarkerResult` payload The following example shows synchronized face, pose, and hand landmarks for a single subject: ```javascript { faceLandmarks: [ { x: 0.4980, y: 0.3120, z: -0.0240 }, // #0..#477 (478 normalized face landmarks) ], poseLandmarks: [ { x: 0.5010, y: 0.2640, z: -0.4120, visibility: 0.9980, presence: 0.9990 }, // #0 nose (33 pose landmarks) ], poseWorldLandmarks: [ { x: 0.0120, y: -0.5480, z: -0.2850, visibility: 0.9980, presence: 0.9990 }, // #0 nose (3D meters) ], leftHandLandmarks: [ { x: 0.6840, y: 0.6120, z: 0.0 }, // #0 WRIST (21 left hand landmarks) ], rightHandLandmarks: [ { x: 0.3180, y: 0.6050, z: 0.0 }, // #0 WRIST (21 right hand landmarks) ] } ``` ## Models  The Holistic Landmarker uses a series of packaged models to perform holistic landmarks detection. These models include face detection, facial 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 landmark model**: detects and tracks 478 3D facial landmarks, including 10 iris 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](https://storage.googleapis.com/mediapipe-models/holistic_landmarker/holistic_landmarker/float16/latest/holistic_landmarker.task) | float 16 | [BlazePose](https://storage.googleapis.com/mediapipe-assets/Model%20Card%20BlazePose%20GHUM%203D.pdf) [FaceLandmarker-V2](https://storage.googleapis.com/mediapipe-assets/Model%20Card%20MediaPipe%20Face%20Mesh%20V2.pdf) [HandLandmarker](https://storage.googleapis.com/mediapipe-assets/Model%20Card%20Hand%20Tracking%20(Lite_Full)%20with%20Fairness%20Oct%202021.pdf) | [Latest](https://storage.googleapis.com/mediapipe-models/holistic_landmarker/holistic_landmarker/float16/latest/holistic_landmarker.task) | ## Landmark coordinates The Holistic Landmarker outputs a total of 553 landmarks that represent the full body: - **Pose landmarks** : 33 landmarks representing key body coordinate points (same as the [Pose Landmarker](https://developers.google.com/edge/mediapipe/solutions/vision/pose_landmarker.md.txt)). - **Face landmarks** : 478 landmarks representing the facial structure including 10 iris landmarks (same as the [Face Landmarker](https://developers.google.com/edge/mediapipe/solutions/vision/face_landmarker)). - **Hand landmarks** : 21 landmarks per hand (42 total) representing knuckle coordinates (same as the [Hand Landmarker](https://developers.google.com/edge/mediapipe/solutions/vision/hand_landmarker.md.txt)).