Abstract:
|
With recent advances in technology and emergence of affordable RGB-D sensors for a
wider range of users, markerless motion capture has become an active field of research
both in computer vision and computer graphics.
In this thesis, we designed a POC (Proof of Concept) for a new tool that enables us
to perform motion capture by using a variable number of commodity RGB-D sensors of
different brands and technical specifications on constraint-less layout environments. The
main goal of this work is to provide a tool with motion capture capabilities by using a
handful of RGB-D sensors, without imposing strong requirements in terms of lighting,
background or extension of the motion capture area. Of course, the number of RGB-D
sensors needed is inversely proportional to their resolution, and directly proportional to
the size of the area to track to.
Built on top of the OpenNI 2 library, we made this POC compatible with most of the nonhigh-end
RGB-D sensors currently available in the market. Due to the lack of resources on
a single computer, in order to support more than a couple of sensors working simultaneously,
we need a setup composed of multiple computers. In order to keep data coherency
and synchronization across sensors and computers, our tool makes use of a semi-automatic
calibration method and a message-oriented network protocol.
From color and depth data given by a sensor, we can also obtain a 3D pointcloud representation
of the environment. By combining pointclouds from multiple sensors, we can
collect a complete and animated 3D pointcloud that can be visualized from any viewpoint.
Given a 3D avatar model and its corresponding attached skeleton, we can use an
iterative optimization method (e.g. Simplex) to find a fit between each pointcloud frame
and a skeleton configuration, resulting in 3D avatar animation when using such skeleton
configurations as key frames. |