3260 papers • 126 benchmarks • 313 datasets
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Faces Learned with an Articulated Model and Expressions is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model and is compared to these models by fitting them to static 3D scans and 4D sequences using the same optimization method.
This work studies learning from a synergy process of 3D Morphable Models (3DMM) and3D facial landmarks to predict complete 3D facial geometry, including 3D alignment, face orientation, and 3D face modeling.
A novel regression framework which makes a balance among speed, accuracy and stability, and a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.
This work introduces a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface and shows that, replacing the expression space of an existing state-of-the-art face model with this model, achieves a lower reconstruction error.
This work presents the first approach that regresses 3D face shape and animatable details that are specific to an individual but change with expression, and introduces a novel detail-consistency loss that disentangles person-specific details from expression-dependent wrinkles.
A statistical model for 3D human faces in varying expression is presented, which decomposes the surface of the face using a wavelet transform, and learns many localized, decorrelated multilinear models on the resulting coefficients.
This paper reviews how different types of models have been used in the literature, then proceeds to define the models and analyze them theoretically, in terms of both their statistical and computational aspects, and performs extensive experimental comparison on the task of model fitting.
This paper proposes an approach to robustly compute correspondences between a large set of facial motion sequences in a fully automatic way using a multilinear model as statistical prior and synthesizes new motion sequences and performs expression recognition.
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