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Found 153 papers, 51 papers with code
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.
Ranked #1 on
Face Verification
on Labeled Faces in the Wild
(using extra training data)
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Face Analysis Project on MXNet
Ranked #1 on
Face Alignment
on IBUG
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Analysis Project on MXNet
Ranked #3 on
Face Detection
on WIDER Face (Medium)
Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces
In this paper, we relax the intra-class constraint of ArcFace to improve the robustness to label noise.
Sample and Computation Redistribution for Efficient Face Detection
Although tremendous strides have been made in uncontrolled face detection, efficient face detection with a low computation cost as well as high precision remains an open challenge.
Ranked #8 on
Face Detection
on WIDER Face (Medium)
Masked Face Recognition Challenge: The InsightFace Track Report
In this workshop, we organize Masked Face Recognition (MFR) challenge and focus on bench-marking deep face recognition methods under the existence of facial masks.
Facial Geometric Detail Recovery via Implicit Representation
Learning a dense 3D model with fine-scale details from a single facial image is highly challenging and ill-posed.
Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing
Specifically, DML-CSR designs a multi-task model which comprises face parsing, binary edge, and category edge detection.
Ranked #1 on
Face Parsing
on Helen
Physically-Based Face Rendering for NIR-VIS Face Recognition
Specifically, we reconstruct 3D face shape and reflectance from a large 2D facial dataset and introduce a novel method of transforming the VIS reflectance to NIR reflectance.
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
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)
Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction
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.
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
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal
Second, we discuss the challenges of face restoration.
Head2Head: Video-based Neural Head Synthesis
In this paper, we propose a novel machine learning architecture for facial reenactment.
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.
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
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
OSTeC: One-Shot Texture Completion
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).
ViTs for SITS: Vision Transformers for Satellite Image Time Series
In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT).
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
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics.
4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications
4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period.
Facial Expression Recognition
Facial Expression Recognition (FER)
SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in analyzing 3D shape meshes.
Single Image 3D Hand Reconstruction with Mesh Convolutions
In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes.
Context-self contrastive pretraining for crop type semantic segmentation
For crop type semantic segmentation from Satellite Image Time Series (SITS) we find performance at parcel boundaries to be a critical bottleneck and explain how CSCL tackles the underlying cause of that problem, improving the state-of-the-art performance in this task.
Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification
In training machine learning models for land cover semantic segmentation there is a stark contrast between the availability of satellite imagery to be used as inputs and ground truth data to enable supervised learning.
End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
To our knowledge, this is the first toolkit that provides generic end-to-end learning for profiling capabilities in either unimodal or multimodal cases.
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
It is thus unclear how these algorithms perform on public face hallucination datasets.
Ranked #1 on
Image Super-Resolution
on WLFW
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures.
Ranked #2 on
Graph Regression
on ZINC 100k
Combining 3D Morphable Models: A Large scale Face-and-Head Model
Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class.
DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation
Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment.
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions.
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.
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss.
Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition
The obtained results show premise for utilization of the extended Aff-Wild, as well as of the developed deep neural architectures for visual analysis of human behaviour in terms of continuous emotion dimensions.
3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
To close the gap between image domains, we create a large-scale dataset of diverse faces with gaze pseudo-annotations, which we extract based on the 3D geometry of the scene, and design a multi-view supervision framework to balance their effect during training.
Binary Graph Neural Networks
In this paper, we present and evaluate different strategies for the binarization of graph neural networks.
DeepSatData: Building large scale datasets of satellite images for training machine learning models
This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models with emphasis placed on dense classification tasks, e. g. semantic segmentation.
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
Speech Emotion Recognition using Semantic Information
In this paper, we propose a novel framework that can capture both the semantic and the paralinguistic information in the signal.
Speech Emotion Recognition
Sound
Audio and Speech Processing
Spatio-temporal Prompting Network for Robust Video Feature Extraction
Then, these video prompts are prepended to the patch embeddings of the current frame as the updated input for video feature extraction.
Robust Conditional Generative Adversarial Networks
Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision.
Locally Adaptive Neural 3D Morphable Models
We present the Locally Adaptive Morphable Model (LAMM), a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes.
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild").
Redesigning Multi-Scale Neural Network for Crowd Counting
In this work, we redesign the multi-scale neural network by introducing a hierarchical mixture of density experts, which hierarchically merges multi-scale density maps for crowd counting.
Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling
In this paper, we introduce a new robust decomposition of images by combining ideas from sparse dictionary learning and PCP.
Inverse Image Frequency for Long-tailed Image Recognition
The long-tailed distribution is a common phenomenon in the real world.
Adaptive Spiral Layers for Efficient 3D Representation Learning on Meshes
The success of deep learning models on structured data has generated significant interest in extending their application to non-Euclidean domains.
A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild
This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the provided emotion videos.
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
We define the regression task in terms of the intrinsic, U-V coordinates of a 3D deformable model that is brought into correspondence with image instances at training time.
Motion deblurring of faces
Notwithstanding, a much less standing mode of variation is motion deblurring, which however presents substantial challenges in face analysis.
An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others.
Visual Data Augmentation through Learning
In addition, the state-of-the-art data-driven methods demand a vast amount of data, hence a standard engineering trick employed is artificial data augmentation for instance by adding into the data cropped and (affinely) transformed images.
Side Information for Face Completion: a Robust PCA Approach
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data.
Robust Kronecker Component Analysis
Dictionary learning and component analysis models are fundamental for learning compact representations that are relevant to a given task (feature extraction, dimensionality reduction, denoising, etc.).
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).
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance.
Informed Non-convex Robust Principal Component Analysis with Features
We revisit the problem of robust principal component analysis with features acting as prior side information.
The Unconstrained Ear Recognition Challenge
In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions.
Joint Multi-view Face Alignment in the Wild
This encompasses two basic problems: i) the detection and deformable fitting steps are performed independently, while the detector might not provide best-suited initialisation for the fitting step, ii) the face appearance varies hugely across different poses, which makes the deformable face fitting very challenging and thus distinct models have to be used (\eg, one for profile and one for frontal faces).
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
As such our network can provide useful correspondence information as a stand-alone system, while when used as an initialization for Statistical Deformable Models we obtain landmark localization results that largely outperform the current state-of-the-art on the challenging 300W benchmark.
Deep Face Deblurring
Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images.
Side Information in Robust Principal Component Analysis: Algorithms and Applications
Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Non-Negative Matrix Factorizations for Multiplex Network Analysis
Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains.
3D Face Morphable Models "In-the-Wild"
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)
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge.
MAGMA: Multi-level accelerated gradient mirror descent algorithm for large-scale convex composite minimization
Composite convex optimization models arise in several applications, and are especially prevalent in inverse problems with a sparsity inducing norm and in general convex optimization with simple constraints.
A Unified Framework for Compositional Fitting of Active Appearance Models
Active Appearance Models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision.
A deep matrix factorization method for learning attribute representations
Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation.
Face frontalization for Alignment and Recognition
The proposed method is assessed in frontal face reconstruction (pose correction), face landmark localization, and pose-invariant face recognition and verification by conducting experiments on $6$ facial images databases.
A Unified Framework for Probabilistic Component Analysis
We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministic component analysis methods.
Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm
A novel procedure is presented in this paper, for training a deep convolutional and recurrent neural network, taking into account both the available training data set and some information extracted from similar networks trained with other relevant data sets.
Deep Neural Network Augmentation: Generating Faces for Affect Analysis
This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i. e., anger, disgust, fear, joy, sadness and surprise), or in terms of valence (i. e., how positive or negative is an emotion) and arousal (i. e., power of the emotion activation).
Ranked #6 on
Facial Expression Recognition (FER)
on RAF-DB
(Avg. Accuracy metric, using extra
training data)
Face Normals "In-The-Wild" Using Fully Convolutional Networks
In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces.
4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications
The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases.
Facial Expression Recognition
Facial Expression Recognition (FER)
Robust Discriminative Response Map Fitting with Constrained Local Models
We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario.
Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences
The superiority of the proposed method against the state-of-the-art time alignment methods, namely the canonical time warping and the generalized time warping, is indicated by the experimental results on both synthetic and real datasets.
Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations
In this paper we introduce a new distance for robustly matching vectors of 3D rotations.
Kernel-PCA Analysis of Surface Normals for Shape-from-Shading
We propose a kernel-based framework for computing components from a set of surface normals.
Merging SVMs with Linear Discriminant Analysis: A Combined Model
A key problem often encountered by many learning algorithms in computer vision dealing with high dimensional data is the so called "curse of dimensionality" which arises when the available training samples are less than the input feature space dimensionality.
RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models
Next, to correct the fittings of a generic model, image congealing (i. e., batch image aliment) is performed by employing only the learnt orthonormal subspace.
Automatic Construction of Deformable Models In-The-Wild
The only requirements of the method are a crude bounding box object detector and a-priori knowledge of the object's shape (e. g. a point distribution model).
Incremental Face Alignment in the Wild
We propose very efficient strategies to update the model and we show that is possible to automatically construct robust discriminative person and imaging condition specific models 'in-the-wild' that outperform state-of-the-art generic face alignment strategies.
Bayesian Active Appearance Models
In this paper we provide the first, to the best of our knowledge, Bayesian formulation of one of the most successful and well-studied statistical models of shape and texture, i. e.
Automatic Construction Of Robust Spherical Harmonic Subspaces
In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images.
Unifying Holistic and Parts-Based Deformable Model Fitting
In this paper we try to marry the previous two frameworks into a unified one that potentially combines the advantages of both.
Active Pictorial Structures
Inspired by the tree structure used in PS, the proposed Active Pictorial Structures (APS) model the appearance of the object using multiple graph-based pairwise normal distributions (Gaussian Markov Random Field) between the patches extracted from the regions around adjacent landmarks.
Joint Unsupervised Deformable Spatio-Temporal Alignment of Sequences
Typically, the problems of spatial and temporal alignment of sequences are considered disjoint.
Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment
Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment.
Deep Canonical Time Warping
Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e. g., aligning visual and acoustic information).
A 3D Morphable Model Learnt From 10,000 Faces
We present Large Scale Facial Model (LSFM) -- a 3D Morphable Model (3DMM) automatically constructed from 9, 663 distinct facial identities.
Estimating Correspondences of Deformable Objects "In-The-Wild"
In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence.
Learning the Multilinear Structure of Visual Data
To extract these modes of variations from visual data, several supervised methods, such as the TensorFaces, that rely on multilinear (tensor) decomposition (e. g., Higher Order SVD) have been developed.
Robust Joint and Individual Variance Explained
Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications.
Face Flow
In this paper, we propose a method for the robust and efficient computation of multi-frame optical flow in an expressive sequence of facial images.
Robust Statistical Face Frontalization
The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions.
MeshGAN: Non-linear 3D Morphable Models of Faces
Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data.
Dense 3D Face Decoding over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders
3D Morphable Models (3DMMs) are statistical models that represent facial texture and shape variations using a set of linear bases and more particular Principal Component Analysis (PCA).
Synthesising 3D Facial Motion from "In-the-Wild" Speech
Synthesising 3D facial motion from speech is a crucial problem manifesting in a multitude of applications such as computer games and movies.
Face Video Generation from a Single Image and Landmarks
In this paper we are concerned with the challenging problem of producing a full image sequence of a deformable face given only an image and generic facial motions encoded by a set of sparse landmarks.
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.
Video-to-Video Translation for Visual Speech Synthesis
Despite remarkable success in image-to-image translation that celebrates the advancements of generative adversarial networks (GANs), very limited attempts are known for video domain translation.
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.
SliderGAN: Synthesizing Expressive Face Images by Sliding 3D Blendshape Parameters
In particular, we propose the SliderGAN which transforms an input face image into a new one according to the continuous values of a statistical blendshape model of facial motion.
Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset
This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset.
Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace
The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task.
Ranked #6 on
Facial Expression Recognition (FER)
on AffectNet
Complement Face Forensic Detection and Localization with FacialLandmarks
In addition, our proposed method consists of two branches and can coherently predict face forensic detection and localization to outperform the previous state-of-the-art techniques on the newly proposed dataset as well as the faceforecsic++ dataset especially on low-quality videos.
Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network
We present the first and the largest study of all facial behaviour tasks learned jointly in a single multi-task, multi-domain and multi-label network, which we call FaceBehaviorNet.
Pixel Adaptive Filtering Units
Next, we employ PAFU in deep neural networks as a replacement of standard convolutional layers to enhance the original architectures with spatially varying computations to achieve considerable performance improvements.
Extracting deep local features to detect manipulated images of human faces
Recent developments in computer vision and machine learning have made it possible to create realistic manipulated videos of human faces, raising the issue of ensuring adequate protection against the malevolent effects unlocked by such capabilities.
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions
Various approaches have been proposed for: i) discrete emotion recognition in terms of the primary facial expressions; ii) emotion analysis in terms of facial Action Units (AUs), assuming a fixed expression intensity; iii) dimensional emotion analysis, in terms of valence and arousal (VA).
Analysing Affective Behavior in the First ABAW 2020 Competition
For the Challenges, we provide a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one annotated for all these three tasks.
Reconstructing the Noise Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
Geometrically Principled Connections in Graph Neural Networks
Graph convolution operators bring the advantages of deep learning to a variety of graph and mesh processing tasks previously deemed out of reach.
ReenactNet: Real-time Full Head Reenactment
Video-to-video synthesis is a challenging problem aiming at learning a translation function between a sequence of semantic maps and a photo-realistic video depicting the characteristics of a driving video.
Learning to Generate Customized Dynamic 3D Facial Expressions
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.
BLSM: A Bone-Level Skinned Model of the Human Mesh
We introduce BLSM, a bone-level skinned model of the human body mesh where bone scales are set prior to template synthesis, rather than the common, inverse practice.
Reconstructing the Noise Variance Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
HeadGAN: One-shot Neural Head Synthesis and Editing
Recent attempts to solve the problem of head reenactment using a single reference image have shown promising results.
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template.
Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework
Affect analysis and recognition can be seen as a dual knowledge generation problem, involving: i) creation of new, large and rich in-the-wild databases and ii) design and training of novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets.
Head2HeadFS: Video-based Head Reenactment with Few-shot Learning
Head reenactment is an even more challenging task, which aims at transferring not only the facial expression, but also the entire head pose from a source person to a target.
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study
Based on this approach, we build FaceBehaviorNet, the first framework for large-scale face analysis, by jointly learning all facial behavior tasks.
Ranked #5 on
Facial Expression Recognition (FER)
on RAF-DB
(Avg. Accuracy metric, using extra
training data)
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.
Analysing Affective Behavior in the second ABAW2 Competition
The Affective Behavior Analysis in-the-wild (ABAW2) 2021 Competition is the second -- following the first very successful ABAW Competition held in conjunction with IEEE FG 2020- Competition that aims at automatically analyzing affect.
Variational Prototype Learning for Deep Face Recognition
Deep face recognition has achieved remarkable improvements due to the introduction of margin-based softmax loss, in which the prototype stored in the last linear layer represents the center of each class.
Poly-NL: Linear Complexity Non-local Layers with Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Ranked #2 on
Face Detection
on WIDER Face (Hard)
Tensor Methods in Computer Vision and Deep Learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions.
Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach
In an attempt to alleviate this computational burden, we propose a fast point cloud simplification method by learning to sample salient points.
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions.
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
The second task required to transfer models trained on the subjects of one or more source motor imagery datasets to perform inference on two target datasets, providing a small set of personalized calibration data for multiple test subjects.
GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation
To address such limitations and alleviate the computational burden, we propose a learnable network to approximate geodesic paths.
Neural Mesh Simplification
Then, we train a sparse attention network to propose candidate triangles based on the edge connectivity of the sampled vertices.
Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control
We present Free-HeadGAN, a person-generic neural talking head synthesis system.
3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling
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.
Dynamic Neural Portraits
We present Dynamic Neural Portraits, a novel approach to the problem of full-head reenactment.
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges
The fifth Affective Behavior Analysis in-the-wild (ABAW) Competition is part of the respective ABAW Workshop which will be held in conjunction with IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2023.
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.
ILSH: The Imperial Light-Stage Head Dataset for Human Head View Synthesis
This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-captured human head dataset designed to support view synthesis academic challenges for human heads.
Latent Alignment with Deep Set EEG Decoders
We delineate a trade-off relationship between increased classification accuracy when alignment is performed at later modeling stages, and susceptibility to class-imbalance in the set of trials that the statistics are computed on.
Rethinking the Domain Gap in Near-infrared Face Recognition
Heterogeneous face recognition (HFR) involves the intricate task of matching face images across the visual domains of visible (VIS) and near-infrared (NIR).
Neural Sign Actors: A diffusion model for 3D sign language production from text
Sign Languages (SL) serve as the primary mode of communication for the Deaf and Hard of Hearing communities.
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.
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond
Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space, or parameter transfer.
Ranked #1 on
Facial Expression Recognition (FER)
on AffectNet
(Accuracy (7 emotion) metric, using extra
training data)
The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition
This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with IEEE CVPR 2024.
AnimateMe: 4D Facial Expressions via Diffusion Models
Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation.
ShapeFusion: A 3D diffusion model for localized shape editing
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.
Design2Cloth: 3D Cloth Generation from 2D Masks
In recent years, there has been a significant shift in the field of digital avatar research, towards modeling, animating and reconstructing clothed human representations, as a key step towards creating realistic avatars.
