3D-Motion-Capture-From-Video

Introduction:

3D motion capture is a technology that allows you to capture human movements and translate them into digital data. This data can then be used to animate 3D models, creating realistic and interactive experiences.

Animation tool(unity):

Unity is a powerful game engine that provides a robust platform for creating 3D games and applications. By combining the capabilities of Python and Unity, we can develop a real-time 3D motion capture system that can be used to enhance gaming and simulation experiences.

Understanding the Workflow:

1. Video Acquisition: Capture real-time video using a webcam or external camera.
2. Pose Estimation: Use Python libraries like OpenCV, TensorFlow, or PyTorch to estimate the 3D pose of the user from the video frames.
3. Data Transmission: Send the estimated pose data from Python to Unity in real-time.
4. 3D Model Animation: In Unity, use the pose data to animate a 3D model, mirroring the user's movements.

To install the 3D Motion Capture From Video, please follow these steps:

1. Clone the repository to your local machine.

2. Install the required dependencies by running the following command:

pip install -r requirement.txt

Python workflow:

1. Video Acquisition

• Camera Setup: Use a webcam or external camera to capture real-time video of the user. Ensure the camera is positioned to capture the desired range of motion.
• Lighting Conditions: Adequate lighting is crucial for accurate pose estimation. Avoid excessive shadows or glare.

2. Pose Estimation with Python

• Library Selection: Choose a suitable Python library for pose estimation, such as OpenCV, TensorFlow, or PyTorch. Each library has its strengths and weaknesses.
• Model Selection: Select a pre-trained model or train your own model based on your specific requirements.
• Keypoint Detection: The library will detect keypoints on the user's body, such as the head, shoulders, elbows, wrists, hips, knees, and ankles.
• Pose Estimation: Using these keypoints, the library will estimate the 3D pose of the user.

Link to code:CLICK HERE TO SEE THE CODE

Data Storage:

• Store the estimated pose data in a structured file format (e.g.,TXT, JSON, CSV, or a custom binary format).

Link to sample data:CLICK HERE TO SEE THE SAMPLE DATA

Unity workflow:

1. Load Pose Data: The Animation_code script loads pose data from the TXT file.
2. Map to Animator: The Animation_code script maps the loaded pose data to animator parameters. This involves associating keypoint coordinates with corresponding body parts on the 3D model.
3. Update Animator: In the Update method, the Animation_code script updates the animator's parameters based on the current pose data, causing the 3D model to animate accordingly.
4. Create Lines: The line_code script creates a LineRenderer component and assigns points to it. These points represent the keypoints from the pose data.
5. Update Line Positions: In the Update method of line_code, the line renderer's positions are updated based on the positions of the points. This visualizes the captured motion as lines connecting the keypoints.

Link to Animation code:CLICK HERE TO SEE THE ANIMATION CODE

Link to line code:CLICK HERE TO SEE THE LINE CODE

Key Challenges and Considerations:

1. Pose Estimation Accuracy: Choose an appropriate pose estimation model based on your requirements..
2. Animator Mapping: Carefully map pose data to animator parameters for accurate representation.
3. Performance Optimization: Consider techniques like multithreading and GPU acceleration for real-time performance.
4. Error Handling: Implement robust error handling to handle unexpected situations.

Applications of 3D Motion Capture from Video:

1. Animation: Creating realistic and expressive animated characters.
2. Gaming: Developing immersive and interactive gaming experiences.
3. Virtual Reality: Creating realistic virtual environments and interactions.
4. Sports Analysis: Analyzing athlete performance and identifying areas for improvement.
5. Biomechanics: Studying human movement patterns and gait analysis.
6. Film and Television: Creating special effects and visual enhancements.

conclusion:

• By combining Python and Unity, we've explored a powerful workflow for creating dynamic 3D models based on real-time motion capture data from video. This approach offers a versatile solution for enriching gaming and simulation experiences.

By following this workflow, we can create a system that allows users to control 3D characters or objects in real-time, opening up new possibilities for gaming, virtual reality, and other applications.