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
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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
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
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, 2016a
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
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)
H. Rhodin, C. Richardt, D. Casas, E. Insafutdinov, M. Shafiei, H.-P. Seidel, B. Schiele and C. Theobalt
Technical Report, 2016b
(arXiv: 1701.00142)
Abstract
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort by possibly needed marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual-reality headset. It combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a new automatically annotated and augmented dataset. Our inside-in method captures full-body motion in general indoor and outdoor scenes, and also crowded scenes.