Gerard Pons-Moll (Senior Researcher)

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

 

    Offers

    If you are interested in doing a PhD on related areas, 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

    Alldieck, T., Magnor, M. A., Bhatnagar, B. L., Theobalt, C., & Pons-Moll, G. (n.d.). Learning to Reconstruct People in Clothing from a Single RGB Camera. In 32nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). Long Beach, CA, USA: IEEE.
    (Accepted/in press)
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    @inproceedings{alldieck19cvpr, TITLE = {Learning to Reconstruct People in Clothing from a Single {RGB} Camera}, AUTHOR = {Alldieck, Thiemo and Magnor, Marcus A. and Bhatnagar, Bharat Lal and Theobalt, Christian and Pons-Moll, Gerard}, PUBLISHER = {IEEE}, YEAR = {2019}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {32nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019)}, ADDRESS = {Long Beach, CA, USA}, }
    Endnote
    %0 Conference Proceedings %A Alldieck, Thiemo %A Magnor, Marcus A. %A Bhatnagar, Bharat Lal %A Theobalt, Christian %A Pons-Moll, Gerard %+ Computer Vision and Multimodal Computing, 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 Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Learning to Reconstruct People in Clothing from a Single RGB Camera : %U http://hdl.handle.net/21.11116/0000-0003-5F97-9 %D 2019 %B 32nd IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2019-06-16 - 2019-06-20 %C Long Beach, CA, USA %B 32nd IEEE Conference on Computer Vision and Pattern Recognition %I IEEE
    Habibie, I., Xu, W., Mehta, D., Pons-Moll, G., & Theobalt, C. (n.d.). In the Wild Human Pose Estimation using Explicit 2D Features and Intermediate 3D Representations. In 32nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). Long Beach, CA, USA: IEEE.
    (Accepted/in press)
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    @inproceedings{habibieCVPR19, TITLE = {In the Wild Human Pose Estimation using Explicit {2D} Features and Intermediate 3D Representations}, AUTHOR = {Habibie, Ikhsanul and Xu, Weipeng and Mehta, Dushyant and Pons-Moll, Gerard and Theobalt, Christian}, PUBLISHER = {IEEE}, YEAR = {2019}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {32nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019)}, ADDRESS = {Long Beach, CA, USA}, }
    Endnote
    %0 Conference Proceedings %A Habibie, Ikhsanul %A Xu, Weipeng %A Mehta, Dushyant %A Pons-Moll, Gerard %A Theobalt, Christian %+ External Organizations Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society %T In the Wild Human Pose Estimation using Explicit 2D Features and Intermediate 3D Representations : %U http://hdl.handle.net/21.11116/0000-0003-6520-7 %D 2019 %B 32nd IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2019-06-16 - 2019-06-20 %C Long Beach, CA, USA %B 32nd IEEE Conference on Computer Vision and Pattern Recognition %I IEEE
    Yu, T., Zheng, Z., Zhong, Y., Zhao, J., Quionhai, D., Pons-Moll, G., & Liu, Y. (n.d.). SimulCap : Single-View Human Performance Capture with Cloth Simulation. In 32nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). Long Beach, CA, USA: IEEE.
    (Accepted/in press)
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    @inproceedings{SimulCap19, TITLE = {{SimulCap} : {S}ingle-View Human Performance Capture with Cloth Simulation}, AUTHOR = {Yu, Tao and Zheng, Zerong and Zhong, Yuan and Zhao, Jianhui and Quionhai, Dai and Pons-Moll, Gerard and Liu, Yebin}, PUBLISHER = {IEEE}, YEAR = {2019}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {32nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019)}, ADDRESS = {Long Beach, CA, USA}, }
    Endnote
    %0 Conference Proceedings %A Yu, Tao %A Zheng, Zerong %A Zhong, Yuan %A Zhao, Jianhui %A Quionhai, Dai %A Pons-Moll, Gerard %A Liu, Yebin %+ External Organizations External Organizations External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society %T SimulCap : Single-View Human Performance Capture with Cloth Simulation : %U http://hdl.handle.net/21.11116/0000-0003-651E-B %D 2019 %B 32nd IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2019-06-16 - 2019-06-20 %C Long Beach, CA, USA %B 32nd IEEE Conference on Computer Vision and Pattern Recognition %I IEEE
    Sattar, H., Pons-Moll, G., & Fritz, M. (2019). Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV 2019). Waikoloa Village, HI, USA: IEEE. doi:10.1109/WACV.2019.00108
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    @inproceedings{sattar19wacv, TITLE = {Fashion is Taking Shape: {U}nderstanding Clothing Preference Based on Body Shape From Online Sources}, AUTHOR = {Sattar, Hosnieh and Pons-Moll, Gerard and Fritz, Mario}, LANGUAGE = {eng}, ISBN = {978-1-7281-1975-5}, DOI = {10.1109/WACV.2019.00108}, PUBLISHER = {IEEE}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {2019 IEEE Winter Conference on Applications of Computer Vision (WACV 2019)}, PAGES = {968--977}, ADDRESS = {Waikoloa Village, HI, USA}, }
    Endnote
    %0 Conference Proceedings %A Sattar, Hosnieh %A Pons-Moll, Gerard %A Fritz, Mario %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources : %G eng %U http://hdl.handle.net/21.11116/0000-0001-B309-B %R 10.1109/WACV.2019.00108 %D 2019 %B IEEE Winter Conference on Applications of Computer Vision %Z date of event: 2019-01-08 - 2019-01-10 %C Waikoloa Village, HI, USA %B 2019 IEEE Winter Conference on Applications of Computer Vision %P 968 - 977 %I IEEE %@ 978-1-7281-1975-5
    Habermann, M., Xu, W., Rohdin, H., Zollhöfer, M., Pons-Moll, G., & Theobalt, C. (2019). NRST: Non-rigid Surface Tracking from Monocular Video. In Pattern Recognition (GCPR 2018). Stuttgart, Germany: Springer. doi:10.1007/978-3-030-12939-2_23
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    @inproceedings{Habermann_GVPR18, TITLE = {{NRST}: {N}on-rigid Surface Tracking from Monocular Video}, AUTHOR = {Habermann, Marc and Xu, Weipeng and Rohdin, Helge and Zollh{\"o}fer, Michael and Pons-Moll, Gerard and Theobalt, Christian}, LANGUAGE = {eng}, ISBN = {978-3-030-12938-5}, DOI = {10.1007/978-3-030-12939-2_23}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Pattern Recognition (GCPR 2018)}, EDITOR = {Brox, Thomas and Bruhn, Andr{\'e}s and Fritz, Mario}, PAGES = {335--348}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11269}, ADDRESS = {Stuttgart, Germany}, }
    Endnote
    %0 Conference Proceedings %A Habermann, Marc %A Xu, Weipeng %A Rohdin, Helge %A Zollhöfer, Michael %A Pons-Moll, Gerard %A Theobalt, Christian %+ Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society External Organizations Computer Graphics, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society %T NRST: Non-rigid Surface Tracking from Monocular Video : %G eng %U http://hdl.handle.net/21.11116/0000-0002-B94C-9 %R 10.1007/978-3-030-12939-2_23 %D 2019 %B 40th German Conference on Pattern Recognition %Z date of event: 2018-10-09 - 2018-10-12 %C Stuttgart, Germany %B Pattern Recognition %E Brox, Thomas; Bruhn, Andrés; Fritz, Mario %P 335 - 348 %I Springer %@ 978-3-030-12938-5 %B Lecture Notes in Computer Science %N 11269
    Habermann, M., Xu, W., Zollhöfer, M., Pons-Moll, G., & Theobalt, C. (2019). LiveCap: Real-time Human Performance Capture from Monocular Video. ACM Transactions on Graphics, 38(2). doi:10.1145/3311970
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    @article{Habermann_TOG19, TITLE = {{LiveCap}: {R}eal-time Human Performance Capture from Monocular Video}, AUTHOR = {Habermann, Marc and Xu, Weipeng and Zollh{\"o}fer, Michael and Pons-Moll, Gerard and Theobalt, Christian}, LANGUAGE = {eng}, ISSN = {0730-0301}, DOI = {10.1145/3311970}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {ACM Transactions on Graphics}, VOLUME = {38}, NUMBER = {2}, EID = {14}, }
    Endnote
    %0 Journal Article %A Habermann, Marc %A Xu, Weipeng %A Zollhöfer, Michael %A Pons-Moll, Gerard %A Theobalt, Christian %+ 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 LiveCap: Real-time Human Performance Capture from Monocular Video : %G eng %U http://hdl.handle.net/21.11116/0000-0002-B947-E %R 10.1145/3311970 %7 2019 %D 2019 %J ACM Transactions on Graphics %V 38 %N 2 %Z sequence number: 14 %I ACM %C New York, NY %@ false
    Sattar, H., Krombholz, K., Pons-Moll, G., & Fritz, M. (2019). Shape Evasion: Preventing Body Shape Inference of Multi-Stage Approaches. Retrieved from http://arxiv.org/abs/1905.11503
    (arXiv: 1905.11503)
    Abstract
    Modern approaches to pose and body shape estimation have recently achieved strong performance even under challenging real-world conditions. Even from a single image of a clothed person, a realistic looking body shape can be inferred that captures a users' weight group and body shape type well. This opens up a whole spectrum of applications -- in particular in fashion -- where virtual try-on and recommendation systems can make use of these new and automatized cues. However, a realistic depiction of the undressed body is regarded highly private and therefore might not be consented by most people. Hence, we ask if the automatic extraction of such information can be effectively evaded. While adversarial perturbations have been shown to be effective for manipulating the output of machine learning models -- in particular, end-to-end deep learning approaches -- state of the art shape estimation methods are composed of multiple stages. We perform the first investigation of different strategies that can be used to effectively manipulate the automatic shape estimation while preserving the overall appearance of the original image.
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    @online{Sattar_arXiv1905.11503, TITLE = {Shape Evasion: Preventing Body Shape Inference of Multi-Stage Approaches}, AUTHOR = {Sattar, Hosnieh and Krombholz, Katharina and Pons-Moll, Gerard and Fritz, Mario}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1905.11503}, EPRINT = {1905.11503}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Modern approaches to pose and body shape estimation have recently achieved strong performance even under challenging real-world conditions. Even from a single image of a clothed person, a realistic looking body shape can be inferred that captures a users' weight group and body shape type well. This opens up a whole spectrum of applications -- in particular in fashion -- where virtual try-on and recommendation systems can make use of these new and automatized cues. However, a realistic depiction of the undressed body is regarded highly private and therefore might not be consented by most people. Hence, we ask if the automatic extraction of such information can be effectively evaded. While adversarial perturbations have been shown to be effective for manipulating the output of machine learning models -- in particular, end-to-end deep learning approaches -- state of the art shape estimation methods are composed of multiple stages. We perform the first investigation of different strategies that can be used to effectively manipulate the automatic shape estimation while preserving the overall appearance of the original image.}, }
    Endnote
    %0 Report %A Sattar, Hosnieh %A Krombholz, Katharina %A Pons-Moll, Gerard %A Fritz, Mario %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations %T Shape Evasion: Preventing Body Shape Inference of Multi-Stage Approaches : %G eng %U http://hdl.handle.net/21.11116/0000-0003-B2E5-1 %U http://arxiv.org/abs/1905.11503 %D 2019 %X Modern approaches to pose and body shape estimation have recently achieved strong performance even under challenging real-world conditions. Even from a single image of a clothed person, a realistic looking body shape can be inferred that captures a users' weight group and body shape type well. This opens up a whole spectrum of applications -- in particular in fashion -- where virtual try-on and recommendation systems can make use of these new and automatized cues. However, a realistic depiction of the undressed body is regarded highly private and therefore might not be consented by most people. Hence, we ask if the automatic extraction of such information can be effectively evaded. While adversarial perturbations have been shown to be effective for manipulating the output of machine learning models -- in particular, end-to-end deep learning approaches -- state of the art shape estimation methods are composed of multiple stages. We perform the first investigation of different strategies that can be used to effectively manipulate the automatic shape estimation while preserving the overall appearance of the original image. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Cryptography and Security, cs.CR,Computer Science, Learning, cs.LG
    Yu, T., Zheng, Z., Guo, K., Zhao, J., Dai, Q., Li, H., … Liu, Y. (2018). DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018). Salt Lake City, UT, USA: IEEE. doi:10.1109/CVPR.2018.00761
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    @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}, ISBN = {978-1-5386-6420-9}, DOI = {10.1109/CVPR.2018.00761}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)}, PAGES = {7287--7296}, 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 %R 10.1109/CVPR.2018.00761 %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 IEEE/CVF Conference on Computer Vision and Pattern Recognition %P 7287 - 7296 %I IEEE %@ 978-1-5386-6420-9
    Omran, M., Lassner,, C., Pons-Moll, G., Gehler, P., & Schiele, B. (2018). Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation. In 3DV 2018 , International Conference on 3D Vision. Verona, Italy: IEEE. doi:10.1109/3DV.2018.00062
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    @inproceedings{omran2018nbf, TITLE = {Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation}, AUTHOR = {Omran, Mohamed and Lassner,, Christoph and Pons-Moll, Gerard and Gehler, Peter and Schiele, Bernt}, LANGUAGE = {eng}, ISBN = {978-1-5386-8425-2 ; 978-1-5386-8426-9}, DOI = {10.1109/3DV.2018.00062}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {3DV 2018 , International Conference on 3D Vision}, PAGES = {484--494}, ADDRESS = {Verona, Italy}, }
    Endnote
    %0 Conference Proceedings %A Omran, Mohamed %A Lassner,, Christoph %A Pons-Moll, Gerard %A Gehler, Peter %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E564-C %R 10.1109/3DV.2018.00062 %D 2018 %B International Conference on 3D Vision %Z date of event: 2018-09-05 - 2018-09-08 %C Verona, Italy %B 3DV 2018 %P 484 - 494 %I IEEE %@ 978-1-5386-8425-2 978-1-5386-8426-9
    Alldieck, T., Magnor, M. A., Xu, W., Theobalt, C., & Pons-Moll, G. (2018a). Video Based Reconstruction of 3D People Models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018). Salt Lake City, UT, USA: IEEE. doi:10.1109/CVPR.2018.00875
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    @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}, ISBN = {978-1-5386-6420-9}, DOI = {10.1109/CVPR.2018.00875}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)}, PAGES = {8387--8397}, 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 %R 10.1109/CVPR.2018.00875 %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 IEEE/CVF Conference on Computer Vision and Pattern Recognition %P 8387 - 8397 %I IEEE %@ 978-1-5386-6420-9
    Von Marcard, T., Henschel, R., Black, M. J., Rosenhahn, B., & Pons-Moll, G. (2018). Recovering Accurate {3D} Human Pose in the Wild Using {IMUs} and a Moving Camera. In Computer Vision -- ECCV 2018. Munich, Germany: Springer. doi:10.1007/978-3-030-01249-6_37
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    @inproceedings{Marcard_ECCV2018, TITLE = {Recovering Accurate {\textbraceleft}{3D}{\textbraceright} Human Pose in the Wild Using {\textbraceleft}{IMUs}{\textbraceright} and a Moving Camera}, AUTHOR = {von Marcard, Timo and Henschel, Roberto and Black, Michael J. and Rosenhahn, Bodo and Pons-Moll, Gerard}, LANGUAGE = {eng}, ISBN = {978-3-030-01248-9}, DOI = {10.1007/978-3-030-01249-6_37}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Computer Vision -- ECCV 2018}, PAGES = {614--631}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11214}, ADDRESS = {Munich, Germany}, }
    Endnote
    %0 Conference Proceedings %A von Marcard, Timo %A Henschel, Roberto %A Black, Michael J. %A Rosenhahn, Bodo %A Pons-Moll, Gerard %+ External Organizations External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Recovering Accurate {3D} Human Pose in the Wild Using {IMUs} and a Moving Camera : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5B61-B %R 10.1007/978-3-030-01249-6_37 %D 2018 %B 15th European Conference on Computer Vision %Z date of event: 2018-09-08 - 2018-09-14 %C Munich, Germany %B Computer Vision -- ECCV 2018 %P 614 - 631 %I Springer %@ 978-3-030-01248-9 %B Lecture Notes in Computer Science %N 11214
    Alldieck, T., Magnor, M. A., Xu, W., Theobalt, C., & Pons-Moll, G. (2018b). Detailed Human Avatars from Monocular Video. In 3DV 2018 , International Conference on 3D Vision. Verona, Italy: IEEE. doi:10.1109/3DV.2018.00022
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    @inproceedings{Alldieck_3DV2018, TITLE = {Detailed Human Avatars from Monocular Video}, AUTHOR = {Alldieck, Thiemo and Magnor, Marcus A. and Xu, Weipeng and Theobalt, Christian and Pons-Moll, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-5386-8425-2 ; 978-1-5386-8426-9}, DOI = {10.1109/3DV.2018.00022}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {3DV 2018 , International Conference on 3D Vision}, PAGES = {98--109}, ADDRESS = {Verona, Italy}, }
    Endnote
    %0 Conference Proceedings %A Alldieck, Thiemo %A Magnor, Marcus A. %A Xu, Weipeng %A Theobalt, Christian %A Pons-Moll, Gerard %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Detailed Human Avatars from Monocular Video : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5C40-F %R 10.1109/3DV.2018.00022 %D 2018 %B International Conference on 3D Vision %Z date of event: 2018-09-05 - 2018-09-08 %C Verona, Italy %B 3DV 2018 %P 98 - 109 %I IEEE %@ 978-1-5386-8425-2 978-1-5386-8426-9
    Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Sridhar, S., Pons-Moll, G., & Theobalt, C. (2018). Single-Shot Multi-person 3D Pose Estimation from Monocular RGB. In 3DV 2018 , International Conference on 3D Vision. Verona, Italy: IEEE. doi:10.1109/3DV.2018.00024
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    @inproceedings{Mehta_3DV2018, TITLE = {Single-Shot Multi-person {3D} Pose Estimation from Monocular {RGB}}, AUTHOR = {Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Sridhar, Srinath and Pons-Moll, Gerard and Theobalt, Christian}, LANGUAGE = {eng}, ISBN = {978-1-5386-8425-2 ; 978-1-5386-8426-9}, DOI = {10.1109/3DV.2018.00024}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {3DV 2018 , International Conference on 3D Vision}, PAGES = {120--130}, ADDRESS = {Verona, Italy}, }
    Endnote
    %0 Conference Proceedings %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 Pose Estimation from Monocular RGB : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5C46-9 %R 10.1109/3DV.2018.00024 %D 2018 %B International Conference on 3D Vision %Z date of event: 2018-09-05 - 2018-09-08 %C Verona, Italy %B 3DV 2018 %P 120 - 130 %I IEEE %@ 978-1-5386-8425-2 978-1-5386-8426-9
    Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., & Pons-Moll, G. (2018). Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time. ACM Transactions on Graphics (Proc. ACM SIGGRAPH Asia 2018), 37(6). doi:10.1145/3272127.3275108
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    @article{DIP:SIGGRAPHAsia:2018, TITLE = {Deep Inertial Poser: {L}earning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time}, AUTHOR = {Huang, Yinghao and Kaufmann, Manuel and Aksan, Emre and Black, Michael J. and Hilliges, Otmar and Pons-Moll, Gerard}, LANGUAGE = {eng}, ISSN = {0730-0301}, ISBN = {978-1-4503-6008-1}, DOI = {10.1145/3272127.3275108}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {ACM Transactions on Graphics (Proc. ACM SIGGRAPH Asia)}, VOLUME = {37}, NUMBER = {6}, EID = {185}, BOOKTITLE = {Proceedings of ACM SIGGRAPH Asia 2018}, }
    Endnote
    %0 Journal Article %A Huang, Yinghao %A Kaufmann, Manuel %A Aksan, Emre %A Black, Michael J. %A Hilliges, Otmar %A Pons-Moll, Gerard %+ External Organizations External Organizations External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9C1E-E %R 10.1145/3272127.3275108 %7 2018 %D 2018 %J ACM Transactions on Graphics %O TOG %V 37 %N 6 %Z sequence number: 185 %I ACM %C New York, NY %@ false %B Proceedings of ACM SIGGRAPH Asia 2018 %O ACM SIGGRAPH Asia 2018 Tokyo, Japan, December 04 - 07, 2018 SA'18 SA 2018 %@ 978-1-4503-6008-1
    Mehta, D., Sotnychenko, O., Mueller, F., Rhodin, H., Xu, W., Pons-Moll, G., & Theobalt, C. (2018). Demo of XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera. In ECCV 2018 Demo Sessions. Munich, Germany. Retrieved from http://gvv.mpi-inf.mpg.de/projects/XNectDemo/
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    BibTeX
    @inproceedings{XNectDemo_ECCV2018, TITLE = {Demo of {XNect}: Real-time Multi-person {3D} Human Pose Estimation with a Single {RGB} Camera}, AUTHOR = {Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Rhodin, Helge and Xu, Weipeng and Pons-Moll, Gerard and Theobalt, Christian}, LANGUAGE = {eng}, URL = {http://gvv.mpi-inf.mpg.de/projects/XNectDemo/}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ECCV 2018 Demo Sessions}, ADDRESS = {Munich, Germany}, }
    Endnote
    %0 Conference Proceedings %A Mehta, Dushyant %A Sotnychenko, Oleksandr %A Mueller, Franziska %A Rhodin, Helge %A Xu, Weipeng %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 Computer Graphics, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society %T Demo of XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera : %G eng %U http://hdl.handle.net/21.11116/0000-0002-F4DC-3 %U http://gvv.mpi-inf.mpg.de/projects/XNectDemo/ %D 2018 %B European Conference on Computer Vision %Z date of event: 2018-09-08 - 2018-09-14 %C Munich, Germany %B ECCV 2018 Demo Sessions %U http://gvv.mpi-inf.mpg.de/projects/XNectDemo/