Human Model Adaptation for Multiview Markerless Motion Capture

Dianyong Zhang,Zhenjiang Miao,Shengyong Chen

doi:10.1155/2013/564214

Abstract

An approach to automatic modeling of individual human bodies using complex shape and pose information. The aim is to address the need for human shape and pose model generation for markerless motion capture. With multi-view markerless motion capture, three-dimensional morphable models are learned from an existing database of registered body scans in different shapes and poses. We estimate the body skeleton and pose parameters from the visual hull mesh reconstructed from multiple human silhouettes. Pose variation of body shapes is implemented by the defined underlying skeleton. The shape parameters are estimated by fitting the morphable model to the silhouettes. It is done relying on extracted silhouettes only. An error function is defined to measure how well the human model fits the input data, and minimize it to get the good estimate result. Further, experiments on some data show the robustness of the method, where the body shape and the initial pose can be obtained automatically.

Highlights

In the area of computer vision, researchers have been working for many years to obtain and analyze real 3D information of an object
We propose a method to automatically adjust a morphable human body model to fit the first frame of the markerless motion capture data
The initial position of human model is computed from the visual hull centroid, and the human skeleton pose can be computed by the visual hull mesh

Summary

Introduction

In the area of computer vision, researchers have been working for many years to obtain and analyze real 3D information of an object. The detection and recovery of human shapes and their 3D poses from images or videos are important problems in computer vision area. Accurate detailed human model can be used for markerless motion capture tracking a subject individual model, which includes information on both body shape and pose. Model-based motion capture is especially suited to markerless motion capture because it can constrain the search space by defining the degrees of freedom of the human skeleton. Initialization of human motion capture always requires the definition of humanoid model approximating the shape, appearance, kinematic structure, and initial pose of the subject to be tracked

Objectives

Results

Conclusion