PoseCraft An Android-based Motion Capture Application empowers users to capture, analyze, and export human motion data for uses such as animation. Utilizing computer vision and machine learning, the app processes recorded videos to detect, track, and estimate 2D and 3D human poses, converting them into usable format. It offers an affordable, user-friendly alternative to high-end systems like Blender, making professional-grade motion capture accessible to individuals and small teams.
Objectives
The primary objectives of the motion capture system are:
1.Provide an Affordable Solution
Develop a cost-effective motion capture tool that eliminates the need for expensive hardware and specialized equipment.
2.User-Friendly Interface
Design an intuitive interface to cater to users with varying levels of technical expertise, ensuring ease of use for animators.
3.Pose Detection
Enable accurate 2D and 3D pose detection and tracking using the smartphone's camera.
4.Compatibility with Existing Platforms
Allow seamless integration with platforms like Blender tools by exporting motion data in formats such as .bvh.
5.Support for Motion Recording and Playback
Allow users to record, save, and replay captured motions for iterative refinement and testing.
Socio-Economic Benefit
PoseCraft aims to make motion capture technology more accessible and practical for individuals and small teams who typically cannot afford high-end tools. Its development supports the following benefits:
1. Affordability for Independent Creators: By removing the need for expensive hardware or studio setups, PoseCraft provides a cost-effective alternative for students, educators, and indie developers working in animation or game development.
2. Learning and Skill Development: The project introduces users to concepts in computer vision, animation, and mobile development, making it useful for training and academic use.
3. Practical Use in Related Fields: Though primarily designed for animation, its core features—pose tracking and motion analysis—may also support simple use cases in sports training, exercise monitoring, or rehabilitation contexts.
4. Encouraging Local Innovation: By providing access to motion capture on mobile devices, the project opens the door for more creators to experiment with animation and interactive content development.
Methodologies
The project followed a structured software engineering methodology, including:
1.Requirement Gathering and Analysis:
I. Identified user needs such as affordability, portability, and ease of use.
II. Focused on specific use cases in animation, gaming, and education.
2.Software Development Life Cycle (SDLC):
I. Applied waterfall model steps like planning, requirement gathering, SRS writing, designing, developing, and testing.
II. Each task was tracked with a timeline and percentage completion.
3. Design Approach:
I. UML diagrams: Composite, Logical, State Dynamics, and Use Case diagrams were used to structure system design.
II. Viewpoints were defined for different aspects: Information, Interaction, Algorithm.
4. Technology Stack:
I. Languages: Java (Android), Python (backend processing).
II. Tools: Android Studio for development, AlphaPose for 2D pose detection, VideoPose3D for 3D pose Detection, Google Colab for model testing.
5. Testing and QA:
I. Test cases covered functionalities such as video capture, pose detection, 3D pose estimation, animation export.
II. Traceability matrix ensured all requirements were tested.
Outcome
The final outcome of the PoseCraft project includes:
1. A fully functional Android motion capture application capable of:
I. Capturing video using a smartphone.
II. Performing 2D and 3D pose estimation.
III. Recording animations.
IV. Exporting motion data in .bvh format compatible with Blender and Unity.
2. A smooth UI/UX with intuitive controls for capturing, processing, previewing, and exporting motion data.
3. Proven performance through successful testing on multiple Android devices.
4. A modular and extensible system architecture that allows future improvements, including cloud storage integration and more advanced analytics.
The project successfully bridged the gap between high-end motion capture systems and the needs of small-scale creators, achieving both technical and social impact objectives.
