5 citations · 10 across the 21 of their papers we have counts for
22 papers
Rethinking 3D Noise: Learning 3D-Aware Video Priors via Optimization-Free Morphological Perturbations
Onat Şahin, Mohammad Altillawi, George Eskandar +2
3D scene representations like NeRF and 3D Gaussian Splatting (3DGS) suffer severe artifacts in sparse-view settings. Recent generative 3D artifact fixers attempt to address this, b…
VideoWeaver: Multimodal Multi-View Video-to-Video Transfer for Embodied Agents
George Eskandar, Fengyi Shen, Mohammad Altillawi +4
Recent progress in video-to-video (V2V) translation has enabled realistic resimulation of embodied AI demonstrations, a capability that allows pretrained robot policies to be trans…
ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation
Liudi Yang, George Eskandar, Fengyi Shen +5
We address the challenge of novel view synthesis from only two input images under large viewpoint changes. Existing regression-based methods lack the capacity to reconstruct unseen…
Physics-Informed Video Diffusion For Shallow Water Equations
Yang Bai, George Eskandar, Ziyuan Liu +1
Traditional fluid dynamics simulation pipelines combine numerical solvers with rendering, producing highly realistic results but at considerable computational cost. Diffusion-based…
CoVAR: Co-generation of Video and Action for Robotic Manipulation via Multi-Modal Diffusion
Liudi Yang, Yang Bai, George Eskandar +5
We present a method to generate video-action pairs that follow text instructions, starting from an initial image observation and the robot's joint states. Our approach automaticall…
DRAW2ACT: Turning Depth-Encoded Trajectories into Robotic Demonstration Videos
Yang Bai, Liudi Yang, George Eskandar +4
Video diffusion models provide powerful real-world simulators for embodied AI but remain limited in controllability for robotic manipulation. Recent works on trajectory-conditioned…
RoboEnvision: A Long-Horizon Video Generation Model for Multi-Task Robot Manipulation
Liudi Yang, Yang Bai, George Eskandar +7
We address the problem of generating long-horizon videos for robotic manipulation tasks. Text-to-video diffusion models have made significant progress in photorealism, language und…
RoboSwap: A GAN-driven Video Diffusion Framework For Unsupervised Robot Arm Swapping
Yang Bai, Liudi Yang, George Eskandar +5
Recent advancements in generative models have revolutionized video synthesis and editing. However, the scarcity of diverse, high-quality datasets continues to hinder video-conditio…
ConsistentDreamer: View-Consistent Meshes Through Balanced Multi-View Gaussian Optimization
Onat Şahin, Mohammad Altillawi, George Eskandar +2
Recent advances in diffusion models have significantly improved 3D generation, enabling the use of assets generated from an image for embodied AI simulations. However, the one-to-m…
An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domains
George Eskandar, Chongzhe Zhang, Abhishek Kaushik +3
3D Object Detectors (3D-OD) are crucial for understanding the environment in many robotic tasks, especially autonomous driving. Including 3D information via Lidar sensors improves…
A Semi-Paired Approach For Label-to-Image Translation
George Eskandar, Shuai Zhang, Mohamed Abdelsamad +3
Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning. Semi-supervised learning has thrived in traditional recognition t…
Towards Pragmatic Semantic Image Synthesis for Urban Scenes
George Eskandar, Diandian Guo, Karim Guirguis +1
The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image tran…
Wavelet-based Unsupervised Label-to-Image Translation
George Eskandar, Mohamed Abdelsamad, Karim Armanious +2
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generati…
Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes
George Eskandar, Youssef Farag, Tarun Yenamandra +3
A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success,…
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging
Karim Guirguis, Johannes Meier, George Eskandar +3
Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neura…
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
Karim Guirguis, Mohamed Abdelsamad, George Eskandar +4
Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learn…
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
Karim Guirguis, Ahmed Hendawy, George Eskandar +3
Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-…
Few-Shot Object Detection in Unseen Domains
Karim Guirguis, George Eskandar, Matthias Kayser +2
Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data by transferring knowledge gained on abundant base classes. FSOD approac…
An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions
George Eskandar, Robert A. Marsden, Pavithran Pandiyan +3
Integrating different representations from complementary sensing modalities is crucial for robust scene interpretation in autonomous driving. While deep learning architectures that…
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving
George Eskandar, Sanjeev Sudarsan, Karim Guirguis +3
Lidar sensors are costly yet critical for understanding the 3D environment in autonomous driving. High-resolution sensors provide more details about the surroundings because they c…
USIS: Unsupervised Semantic Image Synthesis
George Eskandar, Mohamed Abdelsamad, Karim Armanious +1
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a photorealistic image is synthesized from a segmentation mask. SIS has mostly been addressed as a…
SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving
George Eskandar, Alexander Braun, Martin Meinke +2
Predicting future 3D LiDAR pointclouds is a challenging task that is useful in many applications in autonomous driving such as trajectory prediction, pose forecasting and decision…