1,267 research outputs found
Generative Face Completion
In this paper, we propose an effective face completion algorithm using a deep
generative model. Different from well-studied background completion, the face
completion task is more challenging as it often requires to generate
semantically new pixels for the missing key components (e.g., eyes and mouths)
that contain large appearance variations. Unlike existing nonparametric
algorithms that search for patches to synthesize, our algorithm directly
generates contents for missing regions based on a neural network. The model is
trained with a combination of a reconstruction loss, two adversarial losses and
a semantic parsing loss, which ensures pixel faithfulness and local-global
contents consistency. With extensive experimental results, we demonstrate
qualitatively and quantitatively that our model is able to deal with a large
area of missing pixels in arbitrary shapes and generate realistic face
completion results.Comment: Accepted by CVPR 201
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
Recently proposed robust 3D face alignment methods establish either dense or
sparse correspondence between a 3D face model and a 2D facial image. The use of
these methods presents new challenges as well as opportunities for facial
texture analysis. In particular, by sampling the image using the fitted model,
a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map
is always incomplete. In this paper, we propose a framework for training Deep
Convolutional Neural Network (DCNN) to complete the facial UV map extracted
from in-the-wild images. To this end, we first gather complete UV maps by
fitting a 3D Morphable Model (3DMM) to various multiview image and video
datasets, as well as leveraging on a new 3D dataset with over 3,000 identities.
Second, we devise a meticulously designed architecture that combines local and
global adversarial DCNNs to learn an identity-preserving facial UV completion
model. We demonstrate that by attaching the completed UV to the fitted mesh and
generating instances of arbitrary poses, we can increase pose variations for
training deep face recognition/verification models, and minimise pose
discrepancy during testing, which lead to better performance. Experiments on
both controlled and in-the-wild UV datasets prove the effectiveness of our
adversarial UV completion model. We achieve state-of-the-art verification
accuracy, , under the CFP frontal-profile protocol only by combining
pose augmentation during training and pose discrepancy reduction during
testing. We will release the first in-the-wild UV dataset (we refer as WildUV)
that comprises of complete facial UV maps from 1,892 identities for research
purposes
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