Gerard Pons-Moll (Research Leader)

Dr. Gerard Pons-Moll

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 605
Phone
+49 681 9325 2135
Fax
+49 681 9325 2099
Email
Get email via email

Group Homepage

 

Please my group website: http://virtualhumans.mpi-inf.mpg.de

 

    Offers

    We have two open positions for PhD in related areas. If you are interested contact me direclty or send your application to d2-application@mpi-inf.mpg.de. We also have projects for bachelor and master theses and research internships of 6 months.

    Publications -- MPII only

    Yu, T., Zheng, Z., Guo, K., Zhao, J., Dai, Q., Li, H., … Liu, Y. (n.d.). DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor. In 31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018). Salt Lake City, UT, USA.
    (Accepted/in press)
    Export
    BibTeX
    @inproceedings{DoubleFusion, TITLE = {{DoubleFusion}: {R}eal-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor}, AUTHOR = {Yu, Tao and Zheng, Zherong and Guo, Kaiwen and Zhao, Jianhui and Dai, Qionghai and Li, Hao and Pons-Moll, Gerard and Liu, Yebin}, LANGUAGE = {eng}, YEAR = {2018}, PUBLREMARK = {Accepted}, BOOKTITLE = {31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018)}, ADDRESS = {Salt Lake City, UT, USA}, }
    Endnote
    %0 Conference Proceedings %A Yu, Tao %A Zheng, Zherong %A Guo, Kaiwen %A Zhao, Jianhui %A Dai, Qionghai %A Li, Hao %A Pons-Moll, Gerard %A Liu, Yebin %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations %T DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1E30-8 %D 2018 %B 31st IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2018-06-18 - 2018-06-22 %C Salt Lake City, UT, USA %B 31st IEEE Conference on Computer Vision and Pattern Recognition
    Alldieck, T., Magnor, M. A., Xu, W., Theobalt, C., & Pons-Moll, G. (n.d.). Video Based Reconstruction of 3D People Models. In 31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018). Salt Lake City, UT, USA.
    (Accepted/in press)
    Export
    BibTeX
    @inproceedings{alldieck2018video, TITLE = {Video Based Reconstruction of {3D} People Models}, AUTHOR = {Alldieck, Thiemo and Magnor, Marcus A. and Xu, Weipeng and Theobalt, Christian and Pons-Moll, Gerard}, LANGUAGE = {eng}, YEAR = {2018}, PUBLREMARK = {Accepted}, BOOKTITLE = {31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018)}, ADDRESS = {Salt Lake City, UT, USA}, }
    Endnote
    %0 Conference Proceedings %A Alldieck, Thiemo %A Magnor, Marcus A. %A Xu, Weipeng %A Theobalt, Christian %A Pons-Moll, Gerard %+ External Organizations External Organizations External Organizations Computer Graphics, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Video Based Reconstruction of 3D People Models : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1E24-6 %D 2018 %B 31st IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2018-06-18 - 2018-06-22 %C Salt Lake City, UT, USA %B 31st IEEE Conference on Computer Vision and Pattern Recognition
    Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Sridhar, S., Pons-Moll, G., & Theobalt, C. (2017). Single-Shot Multi-Person 3D Body Pose Estimation From Monocular RGB Input. Retrieved from http://arxiv.org/abs/1712.03453
    (arXiv: 1712.03453)
    Abstract
    We propose a new efficient single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our fully convolutional DNN-based approach jointly infers 2D and 3D joint locations on the basis of an extended 3D location map supported by body part associations. This new formulation enables the readout of full body poses at a subset of visible joints without the need for explicit bounding box tracking. It therefore succeeds even under strong partial body occlusions by other people and objects in the scene. We also contribute the first training data set showing real images of sophisticated multi-person interactions and occlusions. To this end, we leverage multi-view video-based performance capture of individual people for ground truth annotation and a new image compositing for user-controlled synthesis of large corpora of real multi-person images. We also propose a new video-recorded multi-person test set with ground truth 3D annotations. Our method achieves state-of-the-art performance on challenging multi-person scenes.
    Export
    BibTeX
    @online{Mehta1712.03453, TITLE = {Single-Shot Multi-Person {3D} Body Pose Estimation From Monocular {RGB} Input}, AUTHOR = {Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Sridhar, Srinath and Pons-Moll, Gerard and Theobalt, Christian}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1712.03453}, EPRINT = {1712.03453}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We propose a new efficient single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our fully convolutional DNN-based approach jointly infers 2D and 3D joint locations on the basis of an extended 3D location map supported by body part associations. This new formulation enables the readout of full body poses at a subset of visible joints without the need for explicit bounding box tracking. It therefore succeeds even under strong partial body occlusions by other people and objects in the scene. We also contribute the first training data set showing real images of sophisticated multi-person interactions and occlusions. To this end, we leverage multi-view video-based performance capture of individual people for ground truth annotation and a new image compositing for user-controlled synthesis of large corpora of real multi-person images. We also propose a new video-recorded multi-person test set with ground truth 3D annotations. Our method achieves state-of-the-art performance on challenging multi-person scenes.}, }
    Endnote
    %0 Report %A Mehta, Dushyant %A Sotnychenko, Oleksandr %A Mueller, Franziska %A Xu, Weipeng %A Sridhar, Srinath %A Pons-Moll, Gerard %A Theobalt, Christian %+ Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society %T Single-Shot Multi-Person 3D Body Pose Estimation From Monocular RGB Input : %G eng %U http://hdl.handle.net/21.11116/0000-0000-438F-4 %U http://arxiv.org/abs/1712.03453 %D 2017 %X We propose a new efficient single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our fully convolutional DNN-based approach jointly infers 2D and 3D joint locations on the basis of an extended 3D location map supported by body part associations. This new formulation enables the readout of full body poses at a subset of visible joints without the need for explicit bounding box tracking. It therefore succeeds even under strong partial body occlusions by other people and objects in the scene. We also contribute the first training data set showing real images of sophisticated multi-person interactions and occlusions. To this end, we leverage multi-view video-based performance capture of individual people for ground truth annotation and a new image compositing for user-controlled synthesis of large corpora of real multi-person images. We also propose a new video-recorded multi-person test set with ground truth 3D annotations. Our method achieves state-of-the-art performance on challenging multi-person scenes. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV