387 citations · 1.1k across the 17 of their papers we have counts for
19 papers
FaceSnap: Real-Time Personalized Lightstage Facial Performance Capture
Rukhshanda Hussain, Noé Artru, Emeline Got +6
Lightstage facial capture produces production-quality digital humans, but it is resource and labor-intensive. Multi-camera setups, hours of computation, and massive data storage cr…
KM-Speaker: Keypoint-Based Style Control for High-Quality Speech-Driven 3D Facial Animation and Dialogue Localization
Arthur Josi, Emeline Got, Abdallah Dib +2
Speech-driven 3D facial animation methods face significant challenges in simultaneously achieving high-fidelity motion and precise artistic control at production quality. Existing…
Geometry-Aware Texture Generation for 3D Head Modeling with Artist-driven Control
Amin Fadaeinejad, Abdallah Dib, Luiz Gustavo Hafemann +6
Creating realistic 3D head assets for virtual characters that match a precise artistic vision remains labor-intensive. We present a novel framework that streamlines this process by…
Model See Model Do: Speech-Driven Facial Animation with Style Control
Yifang Pan, Karan Singh, Luiz Gustavo Hafemann
Speech-driven 3D facial animation plays a key role in applications such as virtual avatars, gaming, and digital content creation. While existing methods have made significant progr…
SEREP: Semantic Facial Expression Representation for Robust In-the-Wild Capture and Retargeting
Arthur Josi, Luiz Gustavo Hafemann, Abdallah Dib +3
Monocular facial performance capture in-the-wild is challenging due to varied capture conditions, face shapes, and expressions. Most current methods rely on linear 3D Morphable Mod…
MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading
Abdallah Dib, Luiz Gustavo Hafemann, Emeline Got +4
Reconstructing an avatar from a portrait image has many applications in multimedia, but remains a challenging research problem. Extracting reflectance maps and geometry from one im…
A Scale-Invariant Trajectory Simplification Method for Efficient Data Collection in Videos
Yang Liu, Luiz Gustavo Hafemann
Training data is a critical requirement for machine learning tasks, and labeled training data can be expensive to acquire, often requiring manual or semi-automated data collection…
Detecting and Matching Related Objects with One Proposal Multiple Predictions
Yang Liu, Luiz G. Hafemann, Michael Jamieson +1
Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time. While for…
Group Activity Detection from Trajectory and Video Data in Soccer
Ryan Sanford, Siavash Gorji, Luiz G. Hafemann +2
Group activity detection in soccer can be done by using either video data or player and ball trajectory data. In current soccer activity datasets, activities are labelled as atomic…
Meta-learning for fast classifier adaptation to new users of Signature Verification systems
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifie…
Universal Adversarial Audio Perturbations
Sajjad Abdoli, Luiz G. Hafemann, Jerome Rony +3
We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenario…
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning syst…
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira +3
Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implication…
Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Methods for learning feature representations for Offline Handwritten Signature Verification have been successfully proposed in recent literature, using Deep Convolutional Neural Ne…
DESlib: A Dynamic ensemble selection library in Python
Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin +1
DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) \emph{dcs}, containing…
Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberat…
Analyzing features learned for Offline Signature Verification using Deep CNNs
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points.…
Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signa…
Offline Handwritten Signature Verification - Literature Review
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification system…