Notice of Violation of IEEE Publication PrinciplesA method of motion capture from the multi-view video sequence

Notice of Violation of IEEE Publication Principles

"A Method of Motion Capture from the Multi-view Video Sequence,"
by Hui-Xiang Xu, Su Yu, Zhi-Hong Yang, Zhi-Chao Li,
in the Proceedings of the 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Vol.4. Aug. 2010. V4-492-V4-495

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Motion Capture Using Joint Skeleton Tracking and Surface Estimation"
J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, H.-P. Seidel,
in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. June 2009 pp.1746-1753

This paper proposes a method of motion capture using joint skeleton tracking and surface estimation from the multi-view video sequence, which can capture the performance of a human or an animal. Given an articulated template model and silhouettes from a multi-view image sequence, our approach recovers not only the movement of the skeleton, but also the possibly non-rigid temporal deformation of the 3D surface. While large scale deformations or fast movements are captured by the skeleton pose and approximate surface skinning, true small scale deformations or are captured by fitting the surface to the silhouette. We further propose a novel optimization scheme for skeleton-based pose estimation that exploits the skeleton\´s tree struct ure to split the optimization problem into a local one and a lower dimensional global one. We show on various sequences that our approach can capture the 3D motion of animals and humans accurately even in the case of rapid movements and wide apparel.