Cookie Consent by Free Privacy Policy Generator 📌 How to visualise MediaPipe’s Hand Tracking in 2D and 3D with Rerun

✅ How to visualise MediaPipe’s Hand Tracking in 2D and 3D with Rerun

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The hand tracking and gesture recognition technology aims to give the ability of the devices to interpret hand movements and gestures as commands or inputs. At the core of this technology, a pre-trained machine-learning model analyses the visual input and identifies hand landmarks and hand gestures. The real applications of such technology vary, as hand movements and gestures can be used to control smart devices. Human-Computer Interaction, Robotics, Gaming, and Augmented Reality are a few of the fields where the potential applications of this technology appear most promising.

In this example, the MediaPipe Gesture and Hand Landmark Detection solutions were utilized to detect and track hand landmarks and recognize gestures. Rerun was employed to visualize the output of the Mediapipe solution over time to make it easy to analyze the behavior.

Logging and visualizing with Rerun

The visualizations in this example were created with the following Rerun code.


For each processed video frame, all data sent to Rerun is associated with the two timelines time and frame_idx.

rr.set_time_sequence("frame_nr", frame_idx)
rr.set_time_nanos("frame_time", frame_time_nano)


The input video is logged as a sequence of Image objects to the Media/Video entity.


Hand landmark points

Logging the hand landmarks involves specifying connections between the points, extracting pose landmark points and logging them to the Rerun SDK. The 2D points are visualized over the video and at a separate entity. Meanwhile, the 3D points allows the creation of a 3D model of the hand for a more comprehensive representation of the hand landmarks.

The 2D and 3D points are logged through a combination of two archetypes. For the 2D points, the Points2D and LineStrips2D archetypes are utilized. These archetypes help visualize the points and connect them with lines, respectively. As for the 3D points, the logging process involves two steps. First, a timeless ClassDescription is logged, that contains the information which maps keypoint ids to labels and how to connect the keypoints. Defining these connections automatically renders lines between them. Mediapipe provides the HAND_CONNECTIONS variable which contains the list of (from, to) landmark indices that define the connections. Second, the actual keypoint positions are logged in 3D Points3D archetype.

Label mapping and keypoint connections

            info=rr.AnnotationInfo(id=0, label="Hand3D"),

rr.log("Hand3D", rr.ViewCoordinates.LEFT_HAND_Y_DOWN, timeless=True)

2D points

Image description

# Log points to the image and Hand Entity
for log_key in ["Media/Points", "Hand/Points"]:
      rr.Points2D(points, radii=10, colors=[255, 0, 0])

# Log connections to the image and Hand Entity [128, 128, 128]
for log_key in ["Media/Connections", "Hand/Connections"]:
      rr.LineStrips2D(np.stack((points1, points2), axis=1), colors=[255, 165, 0])

3D points

Image description

        keypoint_ids=[i for i in range(len(landmark_positions_3d))],


Image description

To showcase gesture recognition, an image of the corresponding gesture emoji is displayed within a TextDocument under the Detection entity.

# Log the detection by using the appropriate image
    rr.TextDocument(f"![Image]({GESTURE_URL + gesture_pic})".strip(), media_type=rr.MediaType.MARKDOWN),

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GitHub logo rerun-io / rerun

Visualize streams of multimodal data. Fast, easy to use, and simple to integrate. Built in Rust using egui.

Build time aware visualizations of multimodal data

Use the Rerun SDK (available for C++, Python and Rust) to log data like images, tensors, point clouds, and text. Logs are streamed to the Rerun Viewer for live visualization or to file for later use.

A short taste

import rerun as rr  # pip install rerun-sdk

rr.connect()  # Connect to a remote viewer
# rr.spawn()  # Spawn a child process with a viewer and connect
#"recording.rrd")  # Stream all logs to disk

# Associate subsequent data with 42 on the “frame” timeline
rr.set_time_sequence("frame", 42))

# Log colored 3D points to the entity at `path/to/points`
rr.log("path/to/points", rr.Points3D(positions, colors=colors

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