Download
VideoPose3D is a deep learning framework that reconstructs 3D human poses from 2D keypoint sequences extracted from videos. It builds on top of convolutional and temporal networks that map 2D joint coordinates over time to consistent 3D skeletons, enabling robust motion capture without specialized sensors. The model is trained on large motion capture datasets and can generalize well to unseen environments by leveraging temporal context for smoothing and error correction. By using only 2D detections (such as those from OpenPose or Detectron), it enables markerless 3D pose estimation with relatively lightweight computational requirements. The framework includes pretrained models, data preprocessing utilities, visualization tools, and evaluation scripts for standard benchmarks like Human3.6M. VideoPose3D has been used widely in computer vision research for human motion understanding, activity recognition, and animation generation.
Features
- End-to-end 3D human pose reconstruction from 2D keypoints
- Temporal convolutional architecture for smooth and consistent trajectories
- Compatible with OpenPose and other 2D keypoint detectors
- Pretrained models and evaluation scripts on standard datasets
- Visualization and data augmentation utilities
- Lightweight implementation optimized for real-time inference
Project Samples
