12 citations · 30 across the 10 of their papers we have counts for
10 papers
GHOST: Fast Category-agnostic Hand-Object Interaction Reconstruction from RGB Videos using Gaussian Splatting
Ahmed Tawfik Aboukhadra, Marcel Rogge, Nadia Robertini +4
Understanding realistic hand-object interactions from monocular RGB videos is essential for AR/VR, robotics, and embodied AI. Existing methods rely on category-specific templates o…
Fast-HaMeR: Boosting Hand Mesh Reconstruction using Knowledge Distillation
Hunain Ahmed Jillani, Ahmed Tawfik Aboukhadra, Ahmed Elhayek +3
Fast and accurate 3D hand reconstruction is essential for real-time applications in VR/AR, human-computer interaction, robotics, and healthcare. Most state-of-the-art methods rely…
SurgeoNet: Realtime 3D Pose Estimation of Articulated Surgical Instruments from Stereo Images using a Synthetically-trained Network
Ahmed Tawfik Aboukhadra, Nadia Robertini, Jameel Malik +3
Surgery monitoring in Mixed Reality (MR) environments has recently received substantial focus due to its importance in image-based decisions, skill assessment, and robot-assisted s…
ShapeGraFormer: GraFormer-Based Network for Hand-Object Reconstruction from a Single Depth Map
Ahmed Tawfik Aboukhadra, Jameel Malik, Nadia Robertini +2
3D reconstruction of hand-object manipulations is important for emulating human actions. Most methods dealing with challenging object manipulation scenarios, focus on hands reconst…
THOR-Net: End-to-end Graformer-based Realistic Two Hands and Object Reconstruction with Self-supervision
Ahmed Tawfik Aboukhadra, Jameel Malik, Ahmed Elhayek +2
Realistic reconstruction of two hands interacting with objects is a new and challenging problem that is essential for building personalized Virtual and Augmented Reality environmen…
HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks
Jameel Malik, Soshi Shimada, Ahmed Elhayek +4
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress h…
HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map
Jameel Malik, Ibrahim Abdelaziz, Ahmed Elhayek +5
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. The state-of-the-art methods directly regress 3D…
Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data
Onorina Kovalenko, Vladislav Golyanik, Jameel Malik +2
Recovery of articulated 3D structure from 2D observations is a challenging computer vision problem with many applications. Current learning-based approaches achieve state-of-the-ar…
DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth
Jameel Malik, Ahmed Elhayek, Fabrizio Nunnari +4
Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation. In contrast to the existing methods which…
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Jameel Malik, Ahmed Elhayek, Didier Stricker
Articulated hand pose estimation is a challenging task for human-computer interaction. The state-of-the-art hand pose estimation algorithms work only with one or a few subjects for…