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Found 9 papers, 1 papers with code
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.
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)
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.
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.
Joint Unsupervised Deformable Spatio-Temporal Alignment of Sequences
Typically, the problems of spatial and temporal alignment of sequences are considered disjoint.
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.
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").
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.
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).
