Search Results for author: Stylianos Ploumpis

Found 18 papers, 10 papers with code

Locally Adaptive Neural 3D Morphable Models

1 code implementation5 Jan 2024 Michail Tarasiou, Rolandos Alexandros Potamias, Eimear O'Sullivan, Stylianos Ploumpis, Stefanos Zafeiriou

We present the Locally Adaptive Morphable Model (LAMM), a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes.

FitMe: Deep Photorealistic 3D Morphable Model Avatars

no code implementations CVPR 2023 Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Baris Gecer, Jiankang Deng, Stefanos Zafeiriou

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.

Dynamic Neural Portraits

no code implementations25 Nov 2022 Michail Christos Doukas, Stylianos Ploumpis, Stefanos Zafeiriou

We present Dynamic Neural Portraits, a novel approach to the problem of full-head reenactment.

Image-to-Image Translation

Neural Mesh Simplification

no code implementations CVPR 2022 Rolandos Alexandros Potamias, Stylianos Ploumpis, Stefanos Zafeiriou

Then, we train a sparse attention network to propose candidate triangles based on the edge connectivity of the sampled vertices.

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 Face Hallucination +3

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

1 code implementation16 May 2021 Baris Gecer, Stylianos Ploumpis, Irene Kotsia, Stefanos Zafeiriou

In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.

3D Face Reconstruction Generative Adversarial Network +1

Learning to Generate Customized Dynamic 3D Facial Expressions

no code implementations ECCV 2020 Rolandos Alexandros Potamias, Jiali Zheng, Stylianos Ploumpis, Giorgos Bouritsas, Evangelos Ververas, Stefanos Zafeiriou

To this end, in this study we employ a deep mesh encoder-decoder like architecture to synthesize realistic high resolution facial expressions by using a single neutral frame along with an expression identification.

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

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

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

1 code implementation ECCV 2020 Baris Gecer, Alexander Lattas, Stylianos Ploumpis, Jiankang Deng, Athanasios Papaioannou, Stylianos Moschoglou, Stefanos Zafeiriou

In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis.

Face Generation

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 Image Generation +3

Combining 3D Morphable Models: A Large scale Face-and-Head Model

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

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class.

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

1 code implementation CVPR 2019 Baris Gecer, Stylianos Ploumpis, Irene Kotsia, Stefanos Zafeiriou

In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.

 Ranked #1 on 3D Face Reconstruction on Florence (Average 3D Error metric)

3D Face Reconstruction Generative Adversarial Network +1

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

no code implementations CVPR 2017 James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, Stefanos Zafeiriou

In this paper, we propose the first, to the best of our knowledge, "in-the-wild" 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an "in-the-wild" texture model.

Ranked #3 on 3D Face Reconstruction on Florence (Average 3D Error metric)

3D Face Reconstruction

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