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Custom template

Helenhayes/CGM2/CGM2.3​

  1. First, please select one of the "Helenhayes/CGM2/CGM2.3" human models according to your own needs and apply dots on it. Record a set of unnamed point data in real-time mode. During the data recording process, avoid marker loss or occlusion. You can also perform some simple actions, such as twisting the waist, waving your hand, lightly lifting your leg, side kick, back kick, turning the wrist, turning the ankle, nodding your head, tilting your head left and right, squatting, etc.
  2. After the recording is completed, please load this set of data in the post-processing mode and ensure that the data starts from the first frame.
  3. Select the corresponding built-in template from the asset library, add it to the asset panel and load the template. Use "Quick Recognition" to identify the unnamed points on the first frame of the data. Once all the identifications are completed, ensure that all the Marker points on the first frame are correctly identified.
  4. Pause the data at the first frame and highlight all frames on the data axis. Click the "Correct" button to identify and correct markers in all frames. After correction, markers in all frames should be recognized. Drag the data axis to check whether the markers in all frames have been correctly identified.
  5. If markers are not correctly identified in a certain frame, use "Quick Identification" to recognize the unnamed markers. If the same marker is not identified across consecutive frames, select the range and use "Quick Identification" on the first frame within the range. After identification, click the "Correct" function to recognize the unnamed points in the selected range.
  6. After all markers are identified in all frames, go to the first frame and click the "Solve" button. After the solving process, the links between points, the human skeleton, and the "V Markers" in the template will be displayed.
  7. If there are still anomalies in the data, refer to "Data Editing → Data Restoration", "Template Training", "Template Identification and Tracking", and "Data Rectification", for instructions on using the data repair functions to fix the data.
  8. Click "File" -- "Save as Model". Load the saved model in real-time mode. The model walks into the motion capture area, and it can be recognized as the human body model in 3D.

Baseline + Toe, Headband (53)​

  1. Record unnamed marker data for the 53-marker full-body model in real-time mode. During data recording, ensure all markers are visible and unobstructed. Perform simple actions such as twisting the waist, waving hands, lightly lifting a leg, side-kicking, back-kicking, rotating wrists, turning ankles, nodding, tilting the head left and right, and squatting.
  2. In the post-processing mode, load this set of recorded unnamed point data, and load a 53-point human body template in the asset panel.
  3. Follow the same steps as mentioned above for customizing the "Helenhayes/CGM2/CGM2.3" human body template.

Creating Custom Templates​

  1. Custom templates can be created by applying markers to objects such as robotic arms, robotic dogs, jump ropes, badminton rackets, etc. After creating a custom template, post-process the data and then load the custom model file in real-time mode for data collection and analysis.
  2. Apply markers to the object to be captured. Maintain a reasonable distance between markers and avoid regular patterns to prevent misidentification.
  3. Move the marked object into the motion capture area and record its movement sequence. Ensure that markers are not lost or obstructed during recording. Then, load the recorded data in post-processing mode.
  4. Open the assets panel, click the "Add Asset" button to create a new asset, enter the asset name, select "Human, " and then click "Create Object."\
9.3.1

  1. Name markers in the "Markers" tab of the assets panel. Go to the first frame of the data, select all frames, and open the "Quick Identification" dialog to identify unnamed points on the object. After identification, create links between points.

  2. Decide whether to create a skeleton based on your needs. For instructions on creating a skeleton, refer to "Data Editing → Create MarkerSet → Create Skeleton (SKB) / Create Skeleton (Calcium)"

  3. Go to the first frame of the data, select all frames, and click "Track Identification." All points on the object will be identified in subsequent frames. If a skeleton has been created, click "Solve, " and the skeleton will be displayed in all subsequent frames.

  4. If some frames have unrecognized points during playback, refer to "Data Editing → Data Restoration", "Template Training", and "Template Identification and Tracking" for instructions on repairing the data.

  5. After repairing the data, save the model and the motion capture data. Switch to real-time mode, load the repaired model file in the assets panel, connect the cameras, and play the footage. When you place the object in the capture area, the 3D view will display the model.

    If model recognition is unstable in real-time mode, it may be due to a sudden increase in the distance between named points on the object during motion. If this occurs, collect another set of data where named points change abruptly or exhibit anomalies, and remedy the data again in post-processing mode.