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Found 10 papers, 2 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.
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).
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.
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.
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.
Domain Separation Networks
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.
Ranked #1 on
Domain Adaptation
on Synth Objects-to-LINEMOD
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 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.
