We present RF-Capture, a system that captures the human figure--ie, a coarse skeleton--
through a wall. RF-Capture tracks the 3D positions of a person's limbs and body parts even …

Raw optical motion capture data often includes errors such as occluded markers, mislabeled
markers, and high frequency noise or jitter. Typically these errors must be fixed by hand-an …

This paper proposes a novel deep learning-based optical motion capture denoising model
encoder-LSTNet-decoder (ELD). ELD uses an autoencoder for manifold learning and …

Fully immersive experiences in AR/VR depend on reconstructing the full body pose of the
user without restricting their motion. In this paper we study the use of body-worn …

A variety of dynamic objects, such as faces, bodies, and cloth, are represented in computer
graphics as a collection of moving spatial landmarks. Spatiotemporal data is inherent in a …

Recent development of per-frame motion extraction method can generate the skeleton of
human motion in real-time with the help of RGB-D cameras such as Kinect. This leads to an …

Optical motion capture systems suffer from marker occlusions resulting in loss of useful
information. This paper addresses the problem of real-time joint localisation of legged …

In a conventional optical motion capture (MoCap) workflow, two processes are needed to
turn captured raw marker sequences into correct skeletal animation sequences. Firstly …

Optical motion capture is a mature contemporary technique for the acquisition of motion
data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps …

Inspired by the development of the matrix completion theories and algorithms, a low-rank
based motion capture (mocap) data refinement method has been developed, which has …