We present an approach for modeling the human body by Sums of spatial Gaussians (SoG),
allowing us to perform fast and high-quality markerless motion capture from multi-view video …

We present a novel method for accurate marker-less capture of articulated skeleton motion
of several subjects in general scenes, indoors and outdoors, even from input filmed with as …

Estimating human pose from videos and image sequences is not only an important
computer vision problem, but also plays very critical role in many real-world applications …

Recent advances in image-based human pose estimation make it possible to capture 3D
human motion from a single RGB video. However, the inherent depth ambiguity and self …

This paper presents a review and comparative study of recent multi-view approaches for
human 3D pose estimation and activity recognition. We discuss the application domain of …

Dynamic multi-person mesh recovery has been a hot topic in 3D vision recently. However,
few works focus on the multi-person motion capture from uncalibrated cameras, which …

We introduce a new class of probabilistic latent variable model called the Implicit Mixture of
Conditional Restricted Boltzmann Machines (imCRBM) for use in human pose tracking. Key …

Action recognition and pose estimation are two closely related topics in understanding
human body movements; information from one task can be leveraged to assist the other, yet …

Marker-less motion capture has seen great progress, but most state-of-the-art approaches
fail to reliably track articulated human body motion with a very low number of cameras, let …

Capturing the skeleton motion and detailed time-varying surface geometry of multiple,
closely interacting peoples is a very challenging task, even in a multicamera setup, due to …