CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.11 - Nov.
Issue No.11 - Nov. (2013 vol.35)
Yebin Liu , Autom. Dept., Tsinghua Univ., Beijing, China
J. Gall , Perceiving Syst. Dept., Max Planck Inst. for Intell. Syst., Tubingen, Germany
C. Stoll , Max Planck Inst. for Inf., Saarland Univ., Saarbrucken, Germany
Qionghai Dai , Autom. Dept., Tsinghua Univ., Beijing, China
Hans-Peter Seidel , Max Planck Inst. for Inf., Saarland Univ., Saarbrucken, Germany
C. Theobalt , Max Planck Inst. for Inf., Saarland Univ., Saarbrucken, Germany
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.47
Capturing the skeleton motion and detailed time-varying surface geometry of multiple, closely interacting peoples is a very challenging task, even in a multicamera setup, due to frequent occlusions and ambiguities in feature-to-person assignments. To address this task, we propose a framework that exploits multiview image segmentation. To this end, a probabilistic shape and appearance model is employed to segment the input images and to assign each pixel uniquely to one person. Given the articulated template models of each person and the labeled pixels, a combined optimization scheme, which splits the skeleton pose optimization problem into a local one and a lower dimensional global one, is applied one by one to each individual, followed with surface estimation to capture detailed nonrigid deformations. We show on various sequences that our approach can capture the 3D motion of humans accurately even if they move rapidly, if they wear wide apparel, and if they are engaged in challenging multiperson motions, including dancing, wrestling, and hugging.
Optimization, Estimation, Image segmentation, Shape, Joints, Humans,image segmentation, Markerless motion capture, multiview video, multiple characters
Yebin Liu, J. Gall, C. Stoll, Qionghai Dai, Hans-Peter Seidel, C. Theobalt, "Markerless Motion Capture of Multiple Characters Using Multiview Image Segmentation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 11, pp. 2720-2735, Nov. 2013, doi:10.1109/TPAMI.2013.47