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Stylianos Ploumpis

15 accepted papers

2024

AnimateMe: 4D Facial Expressions via Diffusion Models

Dimitrios Gerogiannis*, Foivos Paraperas Papantoniou, Rolandos Alexandros Potamias, Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis +1
ECCV 2024poster

"The field of photorealistic 3D avatar reconstruction and generation has garnered significant attention in recent years; however, animating such avatars remains challenging. Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation. In this work,…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

Locally Adaptive Neural 3D Morphable Models

Michail Tarasiou, Rolandos Alexandros Potamias, Eimear O'Sullivan, Stylianos Ploumpis, Stefanos Zafeiriou
CVPR 2024poster

We present the Locally Adaptive Morphable Model (LAMM) a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes. We train our architecture following a simple self-supervised training scheme in which input displacements over a set of sparse control vertices are…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Shapefusion: 3D localized human diffusion models

Rolandos Alexandros Potamias*, Michael Tarasiou, Stylianos Ploumpis, Stefanos Zafeiriou
ECCV 2024poster

"In the realm of 3D computer vision, parametric models have emerged as a ground-breaking methodology for the creation of realistic and expressive 3D avatars. Traditionally, they rely on Principal Component Analysis (PCA), given its ability to decompose data to an orthonormal space that maximally cap…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

FitMe: Deep Photorealistic 3D Morphable Model Avatars

Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Baris Gecer, Jiankang Deng, Stefanos Zafeiriou
CVPR 2023poster

In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appea…

Cited by 35Source ↗PDF ↗Scholar ↗
2023

Handy: Towards a High Fidelity 3D Hand Shape and Appearance Model

Rolandos Alexandros Potamias, Stylianos Ploumpis, Stylianos Moschoglou, Vasileios Triantafyllou, Stefanos Zafeiriou
CVPR 2023poster

Over the last few years, with the advent of virtual and augmented reality, an enormous amount of research has been focused on modeling, tracking and reconstructing human hands. Given their power to express human behavior, hands have been a very important, but challenging component of the human body.…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗
2022

3D Human Tongue Reconstruction From Single "In-the-Wild" Images

Stylianos Ploumpis, Stylianos Moschoglou, Vasileios Triantafyllou, Stefanos Zafeiriou
CVPR 2022oral

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. Since the introduction…

Cited by 7PDF ↗code ↗Scholar ↗
2022

MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis

Athanasios Papaioannou, Baris Gecer, Shiyang Cheng, Grigorios G. Chrysos, Jiankang Deng, Eftychia Fotiadou +12
ECCV 2022poster

"Recently, Deep Neural Networks (DNNs) have been shown to outperform traditional methods in many disciplines such as computer vision, speech recognition and natural language processing. A prerequisite for the successful application of DNNs is the big number of data. Even though various facial datase…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2020

AvatarMe: Realistically Renderable 3D Facial Reconstruction "In-the-Wild"

Alexandros Lattas, Stylianos Moschoglou, Baris Gecer, Stylianos Ploumpis, Vasileios Triantafyllou, Abhijeet Ghosh +1
CVPR 2020poster

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. Nevertheless, to the best…

Cited by 201PDF ↗code ↗Scholar ↗
2020

Learning to Generate Customized Dynamic 3D Facial Expressions

Rolandos Alexandros Potamias, Jiali Zheng, Stylianos Ploumpis, Giorgos Bouritsas, Evangelos Ververas, Stefanos Zafeiriou
ECCV 2020poster

Recent advances in deep learning have significantly pushed the state-of-the-art in photorealistic video animation given a single image. In this paper, we extrapolate those advances to the 3D domain, by studying 3D image-to-video translation with a particular focus on 4D facial expressions. Although…

Cited by 25Source ↗PDF ↗Scholar ↗
2020

Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks

Baris Gecer, Alexandros Lattas, Stylianos Ploumpis, Jiankang Deng, Athanasios Papaioannou, Stylianos Moschoglou +1
ECCV 2020poster

Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of the facial surface. Nevertheless, these models cannot represent faithfully either the facial texture or t…

Cited by 82Source ↗PDF ↗code ↗Scholar ↗
2019

Combining 3D Morphable Models: A Large Scale Face-And-Head Model

Stylianos Ploumpis, Haoyang Wang, Nick Pears, William A. P. Smith, Stefanos Zafeiriou
CVPR 2019oral

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class. In this context, we identify an interesting question that has previously not received research attention: is it possible to combine two or more 3DMMs that (a) are built usin…

Cited by 104PDF ↗Scholar ↗
2019

GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction

Baris Gecer, Stylianos Ploumpis, Irene Kotsia, Stefanos Zafeiriou
CVPR 2019poster

In the past few years, a lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs). In the most recent works, differentiable renderers were employed in order to learn the relationship between…

Cited by 414PDF ↗code ↗Scholar ↗
2019

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, Stefanos Zafeiriou
ICCV 2019poster

Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics. In this paper, we focus on 3D deformable shapes that share a common topological structure, such as human faces and bodies. Morphable Models and their variants, despite their linear formul…

Cited by 192PDF ↗code ↗Scholar ↗
2017

3D Face Morphable Models "In-The-Wild"

James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, Stefanos Zafeiriou
CVPR 2017spotlight

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as exp…

Cited by 213PDF ↗Scholar ↗