Face Alignment
98 papers with code • 26 benchmarks • 17 datasets
Face alignment is the task of identifying the geometric structure of faces in digital images, and attempting to obtain a canonical alignment of the face based on translation, scale, and rotation.
( Image credit: 3DDFA_V2 )
Libraries
Use these libraries to find Face Alignment models and implementationsLatest papers
Faceptor: A Generalist Model for Face Perception
This design enhances the unification of model structure while improving application efficiency in terms of storage overhead.
A survey and classification of face alignment methods based on face models
Face alignment is the process of fitting the landmarks in a face model to the respective ground truth positions in an input image containing a face.
Toward High Quality Facial Representation Learning
To improve the facial representation quality, we use feature map of a pre-trained visual backbone as a supervision item and use a partially pre-trained decoder for mask image modeling.
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
To solve this problem, we propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of semantic ambiguity.
DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment
Thanks to the proposed fusion module, our method is robust not only to occlusion and large pitch and roll view angles, which is the benefit of our image space approach, but also to noise and large yaw angles, which is the benefit of our model space method.
Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models
Our Landmark DEQ (LDEQ) achieves state-of-the-art performance on the challenging WFLW facial landmark dataset, reaching $3. 92$ NME with fewer parameters and a training memory cost of $\mathcal{O}(1)$ in the number of recurrent modules.
SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild
One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images.
Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos
Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements.
Shape Preserving Facial Landmarks with Graph Attention Networks
Top-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance.
VToonify: Controllable High-Resolution Portrait Video Style Transfer
Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency.