Case Study
Case Study
Dynamic Pose Detection and Motion Application to 3D Avatars
Set up a Meeting
An innovative tech start-up in the gaming and virtual meetings industry approached SpringCT with a challenge to develop a solution for capturing human motion from video streams and applying it to 3D avatars in real-time. The client envisioned an immersive, interactive platform where user movements could be mirrored seamlessly by digital avatars.
This project presented complexities in achieving smooth, real-time animations at 60 frames per second. Leveraging its technical expertise, SpringCT designed and implemented a library module, MoCap , using Google's MediaPipe and custom algorithms. This lightweight solution brought advanced motion capture capabilities to standard webcams.
Product Features
The
MoCap
library provides an intuitive and efficient framework
for motion mapping of the upper body, including the
spine, hands, head, and face. The key features
include
Hands Motion Capture
Real-time mapping of left- and right-hand movements
with high accuracy.
Spine and Head Tracking
Captures spine and head rotations (left, right,
forward, backward) and applies them to the avatar.
Facial Expression Mapping
Reflects real-time face gestures, such as smiles,
eyebrow movements, and eye blinking.
Ease of Integration
A modular design allows seamless embedding into any
web application.
Key Technical Achievements
Depth Estimation
Google's MediaPipe provides 2D coordinates for body
landmarks, making it challenging to accurately infer
3D movements. Custom depth estimation techniques were
devised to address this limitation.
Body Occlusion
Instances of partial body occlusion due to clothing or
other objects posed challenges for precise motion
detection. Fine-tuning algorithms and leveraging
MediaPipe's resilience to occlusions mitigated this
issue.
Synchronization and Real-Time Processing
Ensuring smooth and accurate movement synchronization
at 60 FPS required optimizing animation logic and
managing computational resources effectively.
Avatar Anatomy Compatibility
Translating human poses into anatomically realistic
movements for a 3D avatar involved careful study and
adaptation of avatar joint structures using Blender.
Technologies Used
- MediaPipe : For accurate pose detection from video streams.
- TypeScript : To manage pose landmark data flow and animation logic.
- Blender : For studying 3D avatar anatomy and ensuring realistic motion rendering.
- React.js : To create an interactive and responsive user interface for testing and deployment.
Results
- Smooth Real-Time Mapping: The system achieved low-latency, real-time detection and mapping of upper body movements, including simple motions like waving and smiling.
- High Accuracy: The library provided reliable motion tracking despite body occlusions, reflecting movements accurately in the avatar.
- User-Friendly Integration: The modular MoCap library enabled straightforward implementation in diverse applications, including virtual meetings and gaming.
- Challenges Addressed: While complex motions such as rapid rotations occasionally introduced minor lag, the system proved highly efficient for most use cases.
Conclusion
- SpringCT successfully demonstrated its expertise by delivering a cutting-edge dynamic pose detection solution that animates avatars in real-time.
- By leveraging Google's MediaPipe and innovative techniques for depth estimation and animation, the MoCap library offers an accessible and powerful tool for immersive user interaction.
- This solution is poised to revolutionize applications in gaming, fitness tracking, and virtual meetings, showcasing SpringCT's ability to address complex technological challenges with precision.
