Irfan Essa’s Academic Activities

Element-Free Elastic Models for Volume Fitting and Capture (IEEEXplore)

Jaeil Choi Szymczak, A. Turk, G. Essa, I.
Georgia Institute of Technology
This paper appears in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
Publication Date: 2006
Volume: 2
On page(s): 2245 - 2252
ISSN: 1063-6919
ISBN: 0-7695-2597-0
Digital Object Identifier: 10.1109/CVPR.2006.110
Posted online: 2006-10-09 11:11:24.0

Abstract

We present a new method of fitting an element-free volumetric model to a sequence of deforming surfaces of a moving object. Given a sequence of visual hulls, we iteratively fit an element-free elastic model to the visual hull in order to extract the optimal pose of the captured volume. The fitting of the volumetric model is acheived by minimizing a combination of elastic potential energy, a surface distance measure, and a self-intersection penalty for each frame. A unique aspect of our work is that the model is mesh free - since the model is represented as a point cloud, it is easy to construct, manipulate and update the model as needed. Additionally, linear elasicity with rotation compensation makes it possible to handle local deformations and large rotations of body parts much more efficiently than other volume fitting approaches. Our experimental results for volume fitting and capture in a multi-view camera setting demonstrate the robustness of element-free elastic models against noise and self-occlusions.

Gabriel Brostow (2004), “Novel Skeletal Representation for Articulated Creatures” PhD Thesis, Georgia Institute of Technology, College of Computing. (Advisor: Irfan Essa) [PDF] [URI]AbstractThis research examines an approach for capturing 3D surface and structural data of moving articulated creatures. Given the task of non-invasively and automatically capturing such data, a methodology and the associated experiments are presented, that apply to multiview videos of the subjects motion. Our thesis states: A functional structure and the timevarying surface of an articulated creature subject are contained in a sequence of its 3D data. A functional structure is one example of the possible arrangements of internal mechanisms (kinematic joints, springs, etc.) that is capable of performing the motions observed in the input data. Volumetric structures are frequently used as shape descriptors for 3D data. The capture of such data is being facilitated by developments in multi-view video and range scanning, extending to subjects that are alive and moving. In this research, we examine vision-based modeling and the related representation of moving articulated creatures using Spines. We define a Spine as a branching axial structure representing the shape and topology of a 3D objects limbs, and capturing the limbs correspondence and motion over time. The Spine concept builds on skeletal representations often used to describe the internal structure of an articulated object and the significant protrusions. Our representation of a Spine provides for enhancements over a 3D skeleton. These enhancements form temporally consistent limb hierarchies that contain correspondence information about real motion data. We present a practical implementation that approximates a Spines joint probability function to reconstruct Spines for synthetic and real subjects that move. In general, our approach combines the objectives of generalized cylinders, 3D scanning, and markerless motion capture to generate baseline models from real puppets, animals, and human subjects.

Irfan Essa (1990), “Contact detection, collision forces and friction for physically based virtual world modeling” MS Thesis, Massachusetts Institute of Technology. Cambridge, Massachusetts, USA.