no code implementations • 23 Apr 2024 • Sam Earle, Filippos Kokkinos, Yuhe Nie, Julian Togelius, Roberta Raileanu
In contrast, text-to-3D methods allow users to specify desired characteristics in natural language, offering a high amount of flexibility and expressivity.
no code implementations • 18 Mar 2024 • Junlin Han, Filippos Kokkinos, Philip Torr
This results in a significant disparity in scale compared to the vast quantities of other types of data.
no code implementations • 13 Feb 2024 • Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos
A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction networks to output 3D objects directly.
1 code implementation • ICCV 2023 • Roman Shapovalov, Yanir Kleiman, Ignacio Rocco, David Novotny, Andrea Vedaldi, Changan Chen, Filippos Kokkinos, Ben Graham, Natalia Neverova
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially.
1 code implementation • 21 Mar 2023 • Ignacio Rocco, Iurii Makarov, Filippos Kokkinos, David Novotny, Benjamin Graham, Natalia Neverova, Andrea Vedaldi
We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos with accompanying parametric body fits.
no code implementations • 26 Jan 2023 • Uriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual, Iurii Makarov, Filippos Kokkinos, Naman Goyal, Andrea Vedaldi, Devi Parikh, Justin Johnson, Yaniv Taigman
We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions.
no code implementations • 6 Jul 2021 • Francesca Babiloni, Ioannis Marras, Filippos Kokkinos, Jiankang Deng, Grigorios Chrysos, Stefanos Zafeiriou
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Ranked #2 on Face Detection on WIDER Face (Hard)
no code implementations • NeurIPS 2021 • Filippos Kokkinos, Iasonas Kokkinos
We present To The Point (TTP), a method for reconstructing 3D objects from a single image using 2D to 3D correspondences learned from weak supervision.
no code implementations • CVPR 2021 • Filippos Kokkinos, Iasonas Kokkinos
Monocular 3D reconstruction of articulated object categories is challenging due to the lack of training data and the inherent ill-posedness of the problem.
no code implementations • ICCV 2021 • Francesca Babiloni, Ioannis Marras, Filippos Kokkinos, Jiankang Deng, Grigorios Chrysos, Stefanos Zafeiriou
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
1 code implementation • ECCV 2020 • Valeriya Pronina, Filippos Kokkinos, Dmitry V. Dylov, Stamatios Lefkimmiatis
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise.
no code implementations • 24 Nov 2019 • Filippos Kokkinos, Ioannis Marras, Matteo Maggioni, Gregory Slabaugh, Stefanos Zafeiriou
Next, we employ PAFU in deep neural networks as a replacement of standard convolutional layers to enhance the original architectures with spatially varying computations to achieve considerable performance improvements.
1 code implementation • CVPR 2019 • Filippos Kokkinos, Stamatios Lefkimmiatis
In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model.
1 code implementation • 16 Jul 2018 • Filippos Kokkinos, Stamatios Lefkimmiatis
Modern approaches try to jointly solve these problems, i. e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise.
1 code implementation • ECCV 2018 • Filippos Kokkinos, Stamatios Lefkimmiatis
Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise.
no code implementations • SEMEVAL 2017 • Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Mal, Nikolaos rakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan, Alex Potamianos, ros
In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter.
no code implementations • EACL 2017 • Filippos Kokkinos, Alexandros Potamianos
We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification.
no code implementations • SEMEVAL 2016 • Elisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Mal, Nikolaos rakis, Haris Papageorgiou, Shrikanth Narayanan, Alex Potamianos, ros