I am currently the first year Ph.D. student in MIT Computer Science and Artificial Intelligence Laboratory. Before that, I received my M.Phil also in the Information Engineering Department, The Chinese University of Hong Kong in 2011, under the supervised of Prof. Xiaoou Tang and Prof. Jianzhuang Liu. I received my B.E. degree in the Computer Science and Technology Department from Tsinghua University, China, in 2009. My current supervisor is Prof. William T. Freeman, and I also worked closely with Prof. Frédo Durand and Dr. Michael Rubinstein. Here is my Curriculum Vitae [PDF].
My research interest includes computer vision, computer graphics, pattern recognition and multimedia processing.
Ph.D., Computer Sci., Massachusetts Institute of Technology, Aug. 2012 - Current
Supervisor: Prof. Willian T. Freeman.
M.Phil., Information Eng., Chinese University of Hong Kong, Aug. 2009 - Jul. 2011
Supervisor: Prof. Xiaoou Tang GPA: 4.0/4.0.
B. Eng., Computer Sci. & Tech, Tsinghua University, China, Aug. 2005 - Jul. 2009
GPA: 92.06/100.0, Ranking: 3/162
T. Xue, M. Rubinstein, N. Wadhwa, A. Levin, F. Durand, W. T. Freeman, “RefractionWiggles for Measuring Fluid Depth and Velocity from Video”, in the Proceedings of European Conference on Computer Vision Zurich (ECCV) 2014, Oral. [PDF] [Slides] [Data] [Website]
T. Xue, Y. Li, J. Liu., X. Tang, “Example-Based 3D Object Reconstruction from Line Drawings”, in the Proceedings of IEEE Computer Society Conference on Computer Vision and Patter Recognition (CVPR) 2012. [PDF] [Poster] [Dataset]
T. Xue, J. Liu, X. Tang, "Symmetric Piecewise Planar Object Reconstruction from a Single Image," in Proceedings of IEEE Computer Society Conference on Computer Vision and Patter Recognition (CVPR) 2011. [PDF]
T. Xue, J. Liu, X. Tang, "Object Cut: Complex 3D object reconstruction through line drawing separation", in Proceedings of IEEE Computer Society Conference on Computer Vision and Patter Recognition (CVPR) 2010. [PDF]
T. Xue, J. Liu, X. Tang, "3D Modeling from a Single View of a Symmetric Object", Transactions on Image Processing (TIP). (Impact factor: 2.848) [PDF]
Y. Li, L. Sun, T. Xue, "Fast Frame-rate Up-conversion of Depth Video via Video Coding", in Proceedings of ACM Multimedia (MM) 2011. (25% acceptance ratio) [PDF]
Y. Li, T. Xue, L. Sun, J. Liu. "Joint Example-based DepthMap Super-Resolution", accepted by IEEE International Conference on Multimedia & Expo (ICME), 2012. [PDF]
Y. Tang, T. Xue, J. Jiang, B. Liu, "Deflation DFA: Remembering History is Adequate," in Proceedings of IEEE International Conference on Communications (ICC), 2010. (39.5% acceptance ratio) [PDF]
We present principled algorithms for measuring the velocity and 3D location of refractive fluids, such as hot air or gas, from natural videos with textured backgrounds. Our main observation is that intensity variations related to movements of refractive fluid elements, as observed by one or more video cameras, are consistent over small space-time volumes. We call these intensity variations “refraction wiggles”, and use them as features for tracking and stereo fusion to recover the fluid motion and depth from video sequences. Based wiggle features, we give algorithms for 1) measuring the (2D, projected) motion of refractive fluids in monocular videos, and 2) recovering the 3D position of points on the fluid from stereo cameras.
We propose a novel approach, called example-based 3D object reconstruction from line drawings. This method is based on the observation that a natural or man-made complex 3D object normally consists of a set of basic 3D objects.
We proposed an automatic 3D reconstruction algorithm from a single view of symmetric object taking advantage of symmetric information.
We propose a new 3D reconstruction method to recover 3D geometry from line drawing. It decomposes a complex line drawing into simple ones using the geometry property of manifold. The propose methods can greatly reduce the searching space in the reconstruction.
We proposed a new semi-supervised learning methods for example image based reranking. We adopts a graph-based formulation to solve this problem. We further deduct a closed form solution and an iterative solution for this problem. Detailed description is in [PDF].
We designed a human tracking framework combining an online detector using color histogram, and an offline detector using HOG, in which occlusion cases are dealt with by a novel EM algorithm. The project won Outstanding undergraduate thesis of Computer Science and Engineering Department, Tsinghua Univ. [PPT].
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
IEEE Transactions on Systems, Man, and Cybernetics (SMC)