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Mohamed Elgharib

17 accepted papers

2023

EventNeRF: Neural Radiance Fields From a Single Colour Event Camera

CVPR 2023poster

Asynchronously operating event cameras find many applications due to their high dynamic range, vanishingly low motion blur, low latency and low data bandwidth. The field saw remarkable progress during the last few years, and existing event-based 3D reconstruction approaches recover sparse point clou…

2023

LiveHand: Real-time and Photorealistic Neural Hand Rendering

ICCV 2023poster

The human hand is the main medium through which we interact with our surroundings, making its digitization an important problem. While there are several works modeling the geometry of hands, little attention has been paid to capturing photo-realistic appearance. Moreover, for applications in extende…

Cited by 19PDFcodeScholar
2022

NeRF for Outdoor Scene Relighting

ECCV 2022poster

"Photorealistic editing of outdoor scenes from photographs requires a profound understanding of the image formation process and an accurate estimation of the scene geometry, reflectance and illumination. A delicate manipulation of the lighting can then be performed while keeping the scene albedo and…

Cited by 150SourcePDFScholar
2022

f-SfT: Shape-From-Template With a Physics-Based Deformation Model

CVPR 2022poster

Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominan…

Cited by 24PDFcodeScholar
2021

EventHands: Real-Time Neural 3D Hand Pose Estimation From an Event Stream

ICCV 2021poster

3D hand pose estimation from monocular videos is a long-standing and challenging problem, which is now seeing a strong upturn. In this work, we address it for the first time using a single event camera, i.e., an asynchronous vision sensor reacting on brightness changes. Our EventHands approach has c…

Cited by 62PDFcodeScholar
2021

High-Fidelity Neural Human Motion Transfer From Monocular Video

CVPR 2021poster

Video-based human motion transfer creates video animations of humans following a source motion. Current methods show remarkable results for tightly-clad subjects. However, the lack of temporally consistent handling of plausible clothing dynamics, including fine and high-frequency details, significan…

Cited by 41PDFScholar
2021

Learning Complete 3D Morphable Face Models From Images and Videos

CVPR 2021poster

Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity and expression geometry, and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different ident…

Cited by 56PDFScholar
2021

Monocular Reconstruction of Neural Face Reflectance Fields

CVPR 2021poster

The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few ap…

Cited by 36PDFScholar
2021

i3DMM: Deep Implicit 3D Morphable Model of Human Heads

CVPR 2021poster

We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expressions of the frontal face, but also models the entire head, including hair. We collect a new dataset consisting of 64…

Cited by 139PDFcodeScholar
2020

Neural Voice Puppetry: Audio-driven Facial Reenactment

ECCV 2020poster

We present Neural Voice Puppetry, a novel approach for audio-driven facial video synthesis. Given an audio sequence of a source person or digital assistant, we generate a photo-realistic output video of a target person that is in sync with the audio of the source input. This audio-driven facial reen…

2020

Self-supervised Outdoor Scene Relighting

ECCV 2020poster

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a solution. Such renderings are synthesized using priors learned from…

Cited by 62SourcePDFScholar
2020

StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images

CVPR 2020oral

StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination. Three-dimensional morphable fa…

Cited by 473PDFScholar
2019

FML: Face Model Learning From Videos

CVPR 2019oral

Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity makes the problem ill-posed. Most existing methods rely on data-driven priors that are built from limited 3D face scans. In…

Cited by 179PDFScholar
2018

A Dataset of Flash and Ambient Illumination Pairs from the Crowd

ECCV 2018poster

Illumination is a critical element of photography and is essential for many computer vision tasks. Flash light is unique in the sense that it is a widely available tool for easily manipulating the scene illumination. We present a dataset of thousands of ambient and flash illumination pairs to enable…

Cited by 49SourcePDFScholar
2018

Learning-based Video Motion Magnification

ECCV 2018poster

Video motion magnification techniques allow us to see small motions previously invisible to the naked eyes, such as those of vibrating airplane wings, or swaying buildings under the influence of the wind. Because the motion is small, the magnification results are prone to noise or excessive blurring…

Cited by 220SourcePDFScholar
2017

Video Reflection Removal Through Spatio-Temporal Optimization

ICCV 2017poster

Reflections can obstruct content during video capture and hence their removal is desirable. Current removal techniques are designed for still images, extracting only one reflection (foreground) and one background layer from the input. When extended to videos, unpleasant artifacts such as temporal fl…

Cited by 46PDFScholar
2015

Video Magnification in Presence of Large Motions

CVPR 2015poster

Video magnification reveals subtle variations that would be otherwise invisible to the naked eye. Current techniques require all motion in the video to be very small, which is unfortunately not always the case. Tiny yet meaningful motions are often combined with larger motions, such as the small vib…

Cited by 156SourcePDFScholar