no code implementations • 10 Oct 2015 • Baris Gecer, Ozge Yalcinkaya, Onur Tasar, Selim Aksoy
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects.
no code implementations • 1 Aug 2017 • Baris Gecer, Vassileios Balntas, Tae-Kyun Kim
In this work, we investigate several methods and strategies to learn deep embeddings for face recognition, using joint sample- and set-based optimization.
1 code implementation • ECCV 2018 • Baris Gecer, Binod Bhattarai, Josef Kittler, Tae-Kyun Kim
We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model.
Ranked #16 on Face Verification on IJB-A
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)
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.
1 code implementation • 18 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.
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.
1 code implementation • CVPR 2021 • Baris Gecer, Jiankang Deng, Stefanos Zafeiriou
Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Adversarial Networks (GANs).
no code implementations • CVPR 2022 • Amin Jourabloo, Baris Gecer, Fernando de la Torre, Jason Saragih, Shih-En Wei, Te-Li Wang, Stephen Lombardi, Danielle Belko, Autumn Trimble, Hernan Badino
Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR).
1 code implementation • 16 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.
1 code implementation • 11 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.
1 code implementation • 18 Mar 2022 • Xingyu Ren, Alexandros Lattas, Baris Gecer, Jiankang Deng, Chao Ma, Xiaokang Yang, Stefanos Zafeiriou
Learning a dense 3D model with fine-scale details from a single facial image is highly challenging and ill-posed.
no code implementations • 15 Sep 2022 • Stathis Galanakis, Baris Gecer, Alexandros Lattas, Stefanos Zafeiriou
In this work, we present a facial 3D Morphable Model, which exploits both of the above, and can accurately model a subject's identity, pose and expression and render it in arbitrary illumination.
no code implementations • 22 Oct 2022 • Muhammed Pektas, Baris Gecer, Aybars Ugur
Despite the recent success of image generation and style transfer with Generative Adversarial Networks (GANs), hair synthesis and style transfer remain challenging due to the shape and style variability of human hair in in-the-wild conditions.
no code implementations • ICCV 2023 • Urwa Muaz, WonDong Jang, Rohun Tripathi, Santhosh Mani, Wenbin Ouyang, Ravi Teja Gadde, Baris Gecer, Sergio Elizondo, Reza Madad, Naveen Nair
Dubbed video generation aims to accurately synchronize mouth movements of a given facial video with driving audio while preserving identity and scene-specific visual dynamics, such as head pose and lighting.
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.