Search Results for author:
Found 16 papers, 8 papers with code
Arc2Face: A Foundation Model of Human Faces
This paper presents Arc2Face, an identity-conditioned face foundation model, which, given the ArcFace embedding of a person, can generate diverse photo-realistic images with an unparalleled degree of face similarity than existing models.
Ranked #1 on
Diffusion Personalization Tuning Free
on AgeDB
Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks
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.
Towards a complete 3D morphable model of the human head
Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity.
Deep Polynomial Neural Networks
We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.
Ranked #1 on
Face Recognition
on CALFW
AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"
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.
AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs
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.
$Π-$nets: Deep Polynomial Neural Networks
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.
Ranked #1 on
Graph Representation Learning
on COMA
Handy: Towards a High Fidelity 3D Hand Shape and Appearance Model
Currently, most of the state-of-the-art reconstruction and pose estimation methods rely on the low polygon MANO model.
Multi-Attribute Robust Component Analysis for Facial UV Maps
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).
3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation
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.
PolyGAN: High-Order Polynomial Generators
Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.
3D human tongue reconstruction from single "in-the-wild" images
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.
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models
Following the remarkable success of diffusion models on image generation, recent works have also demonstrated their impressive ability to address a number of inverse problems in an unsupervised way, by properly constraining the sampling process based on a conditioning input.
FitMe: Deep Photorealistic 3D Morphable Model Avatars
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.
FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models
This model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an "in-the-wild" 2D facial image.
AnimateMe: 4D Facial Expressions via Diffusion Models
Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation.
