EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
arXiv:1609.07306
Abstract
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort by possibly needed marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. Alternative suit-based systems use several inertial measurement units or an exoskeleton to capture motion. This makes capturing independent of a confined volume, but requires substantial, often constraining, and hard to set up body instrumentation. We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset. It combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a large new dataset. Our inside-in method captures full-body motion in general indoor and outdoor scenes, and also crowded scenes with many people in close vicinity. The captured user can freely move around, which enables reconstruction of larger-scale activities and is particularly useful in virtual reality to freely roam and interact, while seeing the fully motion-captured virtual body.
arXiv commentSIGGRAPH Asia 2016
References in corpus (12)
- DeepPose: Human Pose Estimation via Deep Neural Networks
- Stacked Hourglass Networks for Human Pose Estimation
- Convolutional Pose Machines
- Fast and Robust Hand Tracking Using Detection-Guided Optimization
- Learning Human Pose Estimation Features with Convolutional Networks
- DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
- Flowing ConvNets for Human Pose Estimation in Videos
- 3D Hand Pose Detection in Egocentric RGB-D Images
- MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
- General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues
- Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data
- Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video