Eldar Insafutdinov (PhD Student)

MSc Eldar Insafutdinov

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 629
Phone
+49 681 9325 2029
Fax
+49 681 9325 2099
Email
Get email via email

Personal Information

Publications

2017
ArtTrack: Articulated Multi-Person Tracking in the Wild
E. Insafutdinov, M. Andriluka, L. Pishchulin, S. Tang, E. Levinkov, B. Andres and B. Schiele
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017
(Accepted/in press)
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele and B. Andres
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017
(Accepted/in press)
2016
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. Gehler and B. Schiele
29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
H. Rhodin, C. Richardt, D. Casas, E. Insafutdinov, M. Shafiei, H.-P. Seidel, B. Schiele and C. Theobalt
ACM Transactions on Graphics (Proc. ACM SIGGRAPH Asia 2016), Volume 35, Number 6, 2016
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
E. Insafutdinov, L. Pishchulin, B. Andres, M. Andriluka and B. Schiele
Computer Vision -- ECCV 2016, 2016
Abstract
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. We evaluate our approach on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at http://pose.mpi-inf.mpg.de