Search Results for author: Filippos Kokkinos

Found 17 papers, 6 papers with code

VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models

no code implementations18 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.

IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation

no code implementations13 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.

3D Reconstruction Text to 3D

Real-time volumetric rendering of dynamic humans

1 code implementation21 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.

3D Reconstruction

Text-To-4D Dynamic Scene Generation

no code implementations26 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.

Scene Generation

Poly-NL: Linear Complexity Non-local Layers with Polynomials

no code implementations6 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.

Face Detection Instance Segmentation +1

To The Point: Correspondence-driven monocular 3D category reconstruction

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.

Learning monocular 3D reconstruction of articulated categories from motion

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.

3D Reconstruction

Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials

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.

Face Detection Instance Segmentation +1

Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

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.

Deblurring Denoising +4

Pixel Adaptive Filtering Units

no code implementations24 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.

Translation

Iterative Residual CNNs for Burst Photography Applications

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.

Demosaicking Denoising

Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network

1 code implementation16 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.

Demosaicking Denoising

Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks

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.

Demosaicking Denoising

Structural Attention Neural Networks for improved sentiment analysis

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.

General Classification Sentiment Analysis +1

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