1 code implementation • 5 Aug 2022 • Ziyi Zhao, Sena Kiciroglu, Hugues Vinzant, Yuan Cheng, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
To evaluate our approach, we introduce a dataset with 3 different physical exercises.
no code implementations • 1 Dec 2021 • Isinsu Katircioglu, Costa Georgantas, Mathieu Salzmann, Pascal Fua
To evaluate this, and because no existing motion prediction datasets depict two closely-interacting subjects, we introduce the LindyHop600K dance dataset.
1 code implementation • ICCV 2021 • Isinsu Katircioglu, Helge Rhodin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data.
no code implementations • 11 Nov 2020 • Isinsu Katircioglu, Helge Rhodin, Victor Constantin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
no code implementations • 18 Jul 2019 • Isinsu Katircioglu, Helge Rhodin, Victor Constantin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
1 code implementation • CVPR 2019 • Helge Rhodin, Victor Constantin, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
To this end, we introduce a self-supervised approach to learning what we call a neural scene decomposition (NSD) that can be exploited for 3D pose estimation.
no code implementations • CVPR 2018 • Helge Rhodin, Jörg Spörri, Isinsu Katircioglu, Victor Constantin, Frédéric Meyer, Erich Müller, Mathieu Salzmann, Pascal Fua
Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets.
no code implementations • 17 May 2016 • Bugra Tekin, Isinsu Katircioglu, Mathieu Salzmann, Vincent Lepetit, Pascal Fua
Most recent approaches to monocular 3D pose estimation rely on Deep Learning.
Ranked #313 on 3D Human Pose Estimation on Human3.6M