Search Results for author: Stylianos Moschoglou

Found 10 papers, 6 papers with code

AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs

1 code implementation11 Dec 2021 Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Baris Gecer, Abhijeet Ghosh, Stefanos Zafeiriou

Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data.

3D Face Reconstruction Face Generation

3D human tongue reconstruction from single "in-the-wild" images

no code implementations CVPR 2022 Stylianos Ploumpis, Stylianos Moschoglou, Vasileios Triantafyllou, Stefanos Zafeiriou

3D face reconstruction from a single image is a task that has garnered increased interest in the Computer Vision community, especially due to its broad use in a number of applications such as realistic 3D avatar creation, pose invariant face recognition and face hallucination.

3D Face Reconstruction Computer Vision +3

Deep Polynomial Neural Networks

1 code implementation20 Jun 2020 Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng, Yannis Panagakis, Stefanos Zafeiriou

We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.

Computer Vision Conditional Image Generation +5

AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"

1 code implementation CVPR 2020 Alexandros Lattas, Stylianos Moschoglou, Baris Gecer, Stylianos Ploumpis, Vasileios Triantafyllou, Abhijeet Ghosh, Stefanos Zafeiriou

Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image.

3D Face Reconstruction Face Generation

$Π-$nets: Deep Polynomial Neural Networks

2 code implementations8 Mar 2020 Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, Stefanos Zafeiriou

Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning.

Audio Classification Computer Vision +3

Towards a complete 3D morphable model of the human head

1 code implementation18 Nov 2019 Stylianos Ploumpis, Evangelos Ververas, Eimear O' Sullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William A. P. Smith, Baris Gecer, Stefanos Zafeiriou

Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity.

Face Model

PolyGAN: High-Order Polynomial Generators

no code implementations19 Aug 2019 Grigorios Chrysos, Stylianos Moschoglou, Yannis Panagakis, Stefanos Zafeiriou

Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.

3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation

no code implementations1 May 2019 Stylianos Moschoglou, Stylianos Ploumpis, Mihalis Nicolaou, Athanasios Papaioannou, Stefanos Zafeiriou

As a result, linear methods such as Principal Component Analysis (PCA) have been mainly utilized towards 3D shape analysis, despite being unable to capture non-linearities and high frequency details of the 3D face - such as eyelid and lip variations.

3D Shape Representation Computer Vision +4

Multi-Attribute Robust Component Analysis for Facial UV Maps

no code implementations15 Dec 2017 Stylianos Moschoglou, Evangelos Ververas, Yannis Panagakis, Mihalis Nicolaou, Stefanos Zafeiriou

In this paper, we propose a novel component analysis technique that is suitable for facial UV maps containing a considerable amount of missing information and outliers, while additionally, incorporates knowledge from various attributes (such as age and identity).

3D Face Alignment Face Alignment +1

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