no code implementations • 8 Feb 2024 • Wamiq Reyaz Para, Abdelrahman Eldesokey, Zhenyu Li, Pradyumna Reddy, Jiankang Deng, Peter Wonka
To the best of our knowledge, our approach is the first to introduce multi-modal conditioning to 3D avatar generation and editing.
no code implementations • 12 Dec 2023 • Abdelrahman Eldesokey, Peter Wonka
We propose a zero-shot approach for consistent Text-to-Animated-Characters synthesis based on pre-trained Text-to-Image (T2I) diffusion models.
no code implementations • 5 Jun 2023 • Ahmed Abdelreheem, Abdelrahman Eldesokey, Maks Ovsjanikov, Peter Wonka
Instead, we propose to exploit the in-context learning capabilities of ChatGPT to generate two different sets of semantic regions for each shape and a semantic mapping between them.
no code implementations • 1 Jun 2023 • Ziliang Xiong, Arvi Jonnarth, Abdelrahman Eldesokey, Joakim Johnander, Bastian Wandt, Per-Erik Forssen
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty.
2 code implementations • 13 Feb 2021 • Abdelrahman Eldesokey, Michael Felsberg
Our proposed approach formulates the upsampling task as a sparse problem and employs the normalized convolutional neural networks to solve it.
1 code implementation • CVPR 2020 • Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist, Mikael Persson
In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction.
no code implementations • 3 Apr 2019 • Adam Nyberg, Abdelrahman Eldesokey, David Bergström, David Gustafsson
When trained and evaluated on KAIST-MS dataset, our proposed methods was shown to produce significantly more realistic and sharp VIS images than the existing state-of-the-art supervised methods.
1 code implementation • 5 Nov 2018 • Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan
In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work.
Ranked #7 on Depth Completion on KITTI Depth Completion
1 code implementation • 30 May 2018 • Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan
To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task.