10 citations · 17 across the 10 of their papers we have counts for
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GHARP: Real-time Gaussian Head Animation from Large-scale Reconstruction Prior
Ali Benlalah, Sepehr Johari, Patricia Vitoria +14
We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a few input images of a subject…
Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks
Guillermo Carbajal, Patricia Vitoria, José Lezama +1
In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp im…
Assessing the Role of Datasets in the Generalization of Motion Deblurring Methods to Real Images
Guillermo Carbajal, Patricia Vitoria, José Lezama +1
Successfully training end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generaliza…
Event-based Image Deblurring with Dynamic Motion Awareness
Patricia Vitoria, Stamatios Georgoulis, Stepan Tulyakov +3
Non-uniform image deblurring is a challenging task due to the lack of temporal and textural information in the blurry image itself. Complementary information from auxiliary sensors…
Analysis of Different Losses for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically co…
An Analysis of Generative Methods for Multiple Image Inpainting
Coloma Ballester, Aurelie Bugeau, Samuel Hurault +2
Image inpainting refers to the restoration of an image with missing regions in a way that is not detectable by the observer. The inpainting regions can be of any size and shape. Th…
Influence of Color Spaces for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing col…
Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition
Guillermo Carbajal, Patricia Vitoria, Mauricio Delbracio +2
Motion blur estimation remains an important task for scene analysis and image restoration. In recent years, the removal of motion blur in photographs has seen impressive progress i…
ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
Patricia Vitoria, Lara Raad, Coloma Ballester
The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, we propose an adversarial learning colorization approach coupled with…
Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks
Patricia Vitoria, Joan Sintes, Coloma Ballester
Image inpainting is the task of filling-in missing regions of a damaged or incomplete image. In this work we tackle this problem not only by using the available visual data but als…