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Isinsu Katircioglu

4 accepted papers

2025

GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control

Mariam Hassan, Sebastian Stapf, Ahmad Rahimi, Pedro M B Rezende, Yasaman Haghighi, David Brüggemann +14
CVPR 2025poster

We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, our model has precise control over object dynamics, ego-agent motion and human poses. GEM generates paired RGB and depth o…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗
2021

Human Detection and Segmentation via Multi-View Consensus

Isinsu Katircioglu, Helge Rhodin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
ICCV 2021poster

Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data. However, existing approaches predominantly rely on restrictive assumptions on appearance and motion. For scenes with dynamic activities and camera motion, we propose a multi-camera fra…

Cited by 3PDF ↗code ↗Scholar ↗
2019

Neural Scene Decomposition for Multi-Person Motion Capture

Helge Rhodin, Victor Constantin, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
CVPR 2019poster

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned features are a valuable starting point to learn f…

Cited by 61PDF ↗Scholar ↗
2018

Learning Monocular 3D Human Pose Estimation From Multi-View Images

Helge Rhodin, Jörg Spörri, Isinsu Katircioglu, Victor Constantin, Frédéric Meyer, Erich Müller +2
CVPR 2018poster

Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Manual annotation is tedious, slow, and error…

Cited by 308Source ↗PDF ↗Scholar ↗