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

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
Articulated Multi-person Tracking in the Wild
E. Insafutdinov, M. Andriluka, L. Pishchulin, S. Tang, E. Levinkov, B. Andres and B. Schiele
Technical Report, 2016
(arXiv: 1612.01465)
Abstract
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is several orders of magnitude faster. We achieve this in two ways: (1) by simplifying and sparsifying the body-part relationship graph and leveraging recent methods for faster inference, and (2) by offloading a substantial share of computation onto a feed-forward convolutional architecture that is able to detect and associate body joints of the same person even in clutter. We use this model to generate proposals for body joint locations and formulate articulated tracking as spatio-temporal grouping of such proposals. This allows to jointly solve the association problem for all people in the scene by propagating evidence from strong detections through time and enforcing constraints that each proposal can be assigned to one person only. We report results on a public MPII Human Pose benchmark and on a new dataset of videos with multiple people. We demonstrate that our model achieves state-of-the-art results while using only a fraction of time and is able to leverage temporal information to improve state-of-the-art for crowded scenes.