2 citations · 3 across the 3 of their papers we have counts for
6 papers
Self-supervised Segmentation via Background Inpainting
Isinsu Katircioglu, Helge Rhodin, Victor Constantin +3
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they h…
GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss
Erhan Gundogdu, Victor Constantin, Shaifali Parashar +4
In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of…
Self-supervised Training of Proposal-based Segmentation via Background Prediction
Isinsu Katircioglu, Helge Rhodin, Victor Constantin +3
While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained…
Neural Scene Decomposition for Multi-Person Motion Capture
Helge Rhodin, Victor Constantin, Isinsu Katircioglu +2
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 networ…
GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping
Erhan Gundogdu, Victor Constantin, Amrollah Seifoddini +3
While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inferenc…
Learning Monocular 3D Human Pose Estimation from Multi-view Images
Helge Rhodin, Jörg Spörri, Isinsu Katircioglu +5
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…