Implementing the described process in Unity using C# involves several steps, including data preprocessing, joint angle to position conversion, local coordinate system definition, velocity computation, foot contact detection, and data normalization. This example will provide a basic structure for these tasks, but keep in mind that some steps, especially those related to data preprocessing and Gaussian filtering, might be more efficiently performed outside of Unity (e.g., in a Python script) and then imported into Unity for use with animations or machine learning.
First, define your skeleton structure, variables for storing mean pose and standard deviations, and any necessary buffers for smoothing operations.
using System.Collections.Generic;
public class MocapProcessor : MonoBehaviour
public Transform rootJoint; // Root joint of the skeleton
public Transform[] joints; // Array of all joints in the skeleton
public float gaussianKernelWidth = 1.0f;
public float contactThreshold = 0.1f; // Threshold for foot contact detection
private Vector3 meanPose;
private Vector3 stdDevPose;
private Vector3 velocityStdDev;
private Vector3 prevRootPosition;
private Quaternion prevRootRotation;
// Initialize variables, e.g., load mean and std dev from preprocessed data
Create a method to process each frame of mocap data, converting joint angles to positions, defining the local coordinate system, computing velocities, detecting foot contacts, and normalizing the data.
// Example for converting joint angles to positions
// In practice, you would use animation data that already has joint positions
Vector3[] jointPositions = new Vector3[joints.Length];
for (int i = 0; i < joints.Length; i++)
jointPositions[i] = joints[i].localPosition; // Assuming localPosition gives the correct position relative to the parent
// Define body's local coordinate system
Vector3 forwardDirection = DefineLocalCoordinateSystem(jointPositions);
// Compute global and rotational velocities
Vector3 globalVelocity = (rootJoint.position - prevRootPosition) / Time.deltaTime;
float rotationalVelocity = Quaternion.Angle(prevRootRotation, rootJoint.rotation) / Time.deltaTime;
bool[] footContacts = DetectFootContacts(jointPositions);
Vector3[] normalizedPositions = NormalizeData(jointPositions);
// Update previous frame variables
prevRootPosition = rootJoint.position;
prevRootRotation = rootJoint.rotation;
// Combine data into your desired format for further use
Vector3 DefineLocalCoordinateSystem(Vector3[] jointPositions)
// Compute forward direction based on shoulders and hips
// Apply Gaussian smoothing if necessary
return Vector3.forward; // Placeholder
bool[] DetectFootContacts(Vector3[] jointPositions)
// Implement foot contact detection based on joint positions and velocities
return new bool[jointPositions.Length]; // Placeholder
Vector3[] NormalizeData(Vector3[] jointPositions)
Vector3[] normalizedPositions = new Vector3[jointPositions.Length];
for (int i = 0; i < jointPositions.Length; i++)
normalizedPositions[i] = (jointPositions[i] - meanPose) / stdDevPose;
return normalizedPositions;
You’ll need to implement or flesh out methods like DefineLocalCoordinateSystem, DetectFootContacts, and any smoothing functions you require. For Gaussian smoothing, consider using a precomputed kernel or a library that can perform the operation.
Integrate this processor with your mocap data playback system. You might be looping through frames of mocap data in an animation clip or processing live mocap data.
- Gaussian Smoothing: Gaussian smoothing might be complex to implement efficiently in Unity as it typically requires convolution operations that are more suited to data processing environments like MATLAB or Python with NumPy.
- Foot Contact Detection: Implementing this accurately may require additional logic to check the velocity and position of the feet.
- Normalization: Ensure you have calculated the mean pose and standard deviations beforehand, possibly using a separate preprocessing step with your entire dataset.
This example provides a basic structure for the tasks described, but each function will need to be tailored to the specifics of your mocap data and the requirements of your application. Unity’s primary strength is in real-time 3D applications, so for heavy data processing tasks (like Gaussian smoothing across many frames), consider preprocessing the data outside of Unity.