MotionBERT
| Property | Value |
|---|---|
| Author | walterzhu |
| Paper | arXiv:2210.06551 |
| Model Variants | Standard (162MB), Lite (61MB) |
| Primary Tasks | 3D Pose Estimation, Action Recognition, Mesh Recovery |
What is MotionBERT?
MotionBERT is a groundbreaking unified framework for human motion analysis that leverages transformer architecture to handle multiple motion-related tasks. It provides a comprehensive solution for understanding human movements in various contexts, from pose estimation to action recognition.
Implementation Details
The model processes 2D skeleton data with 17 body keypoints in H36M format, supporting sequences up to 243 frames. It produces rich motion representations that can be adapted for various downstream tasks. The architecture includes both standard (162MB) and lite (61MB) versions, with the lite version offering similar performance with reduced computational overhead.
- Supports variable input lengths up to 243 frames
- Works with 17-point body keypoint system
- Provides 512-dimensional feature representations per joint
- Includes efficient data preprocessing pipeline
Core Capabilities
- 3D Pose Estimation: Achieves 37.2mm MPJPE on H36M dataset
- Action Recognition: 97.2% Top-1 accuracy on NTU60 x-sub
- Mesh Recovery: 88.1mm MPVE on 3DPW dataset
- In-the-wild video inference support
Frequently Asked Questions
Q: What makes this model unique?
MotionBERT's uniqueness lies in its unified approach to human motion analysis, handling multiple tasks with a single backbone architecture. It provides state-of-the-art performance across different motion-related tasks while maintaining computational efficiency.
Q: What are the recommended use cases?
The model is ideal for applications requiring human motion analysis, including: 3D pose estimation from video, action recognition in surveillance or gaming, human mesh recovery for animation, and general motion representation learning for custom applications.
