251 research outputs found
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Face Super-Resolution (SR) is a domain-specific super-resolution problem. The
specific facial prior knowledge could be leveraged for better super-resolving
face images. We present a novel deep end-to-end trainable Face Super-Resolution
Network (FSRNet), which makes full use of the geometry prior, i.e., facial
landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR)
face images without well-aligned requirement. Specifically, we first construct
a coarse SR network to recover a coarse high-resolution (HR) image. Then, the
coarse HR image is sent to two branches: a fine SR encoder and a prior
information estimation network, which extracts the image features, and
estimates landmark heatmaps/parsing maps respectively. Both image features and
prior information are sent to a fine SR decoder to recover the HR image. To
further generate realistic faces, we propose the Face Super-Resolution
Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss
into FSRNet. Moreover, we introduce two related tasks, face alignment and
parsing, as the new evaluation metrics for face SR, which address the
inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark
experiments show that FSRNet and FSRGAN significantly outperforms state of the
arts for very LR face SR, both quantitatively and qualitatively. Code will be
made available upon publication.Comment: Chen and Tai contributed equally to this pape
Face Hallucination With Finishing Touches
Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both tasks seamlessly for daily-life unconstrained face images. In this paper, we present a novel Vivid Face Hallucination Generative Adversarial Network (VividGAN) for simultaneously super-resolving and frontalizing tiny non-frontal face images. VividGAN consists of coarse-level and fine-level Face Hallucination Networks (FHnet) and two discriminators, i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i.e., fine-grained facial components, to attain a frontal HR face image with authentic details. In the fine-level FHnet, we also design a facial component-aware module that adopts the facial geometry guidance as clues to accurately align and merge the frontal coarse HR face and prior information. Meanwhile, two-level discriminators are designed to capture both the global outline of a face image as well as detailed facial characteristics. The Coarse-D enforces the coarsely hallucinated faces to be upright and complete while the Fine-D focuses on the fine hallucinated ones for sharper details. Extensive experiments demonstrate that our VividGAN achieves photo-realistic frontal HR faces, reaching superior performance in downstream tasks, i.e., face recognition and expression classification, compared with other state-of-the-art methods
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal
Face Restoration (FR) aims to restore High-Quality (HQ) faces from
Low-Quality (LQ) input images, which is a domain-specific image restoration
problem in the low-level computer vision area. The early face restoration
methods mainly use statistic priors and degradation models, which are difficult
to meet the requirements of real-world applications in practice. In recent
years, face restoration has witnessed great progress after stepping into the
deep learning era. However, there are few works to study deep learning-based
face restoration methods systematically. Thus, this paper comprehensively
surveys recent advances in deep learning techniques for face restoration.
Specifically, we first summarize different problem formulations and analyze the
characteristic of the face image. Second, we discuss the challenges of face
restoration. Concerning these challenges, we present a comprehensive review of
existing FR methods, including prior based methods and deep learning-based
methods. Then, we explore developed techniques in the task of FR covering
network architectures, loss functions, and benchmark datasets. We also conduct
a systematic benchmark evaluation on representative methods. Finally, we
discuss future directions, including network designs, metrics, benchmark
datasets, applications,etc. We also provide an open-source repository for all
the discussed methods, which is available at
https://github.com/TaoWangzj/Awesome-Face-Restoration.Comment: 21 pages, 19 figure
Facial Attribute Capsules for Noise Face Super Resolution
Existing face super-resolution (SR) methods mainly assume the input image to
be noise-free. Their performance degrades drastically when applied to
real-world scenarios where the input image is always contaminated by noise. In
this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with
the problem of high-scale super-resolution of noisy face image. Capsule is a
group of neurons whose activity vector models different properties of the same
entity. Inspired by the concept of capsule, we propose an integrated
representation model of facial information, which named Facial Attribute
Capsule (FAC). In the SR processing, we first generated a group of FACs from
the input LR face, and then reconstructed the HR face from this group of FACs.
Aiming to effectively improve the robustness of FAC to noise, we generate FAC
in semantic, probabilistic and facial attributes manners by means of integrated
learning strategy. Each FAC can be divided into two sub-capsules: Semantic
Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial
attribute in detail from two aspects of semantic representation and probability
distribution. The group of FACs model an image as a combination of facial
attribute information in the semantic space and probabilistic space by an
attribute-disentangling way. The diverse FACs could better combine the face
prior information to generate the face images with fine-grained semantic
attributes. Extensive benchmark experiments show that our method achieves
superior hallucination results and outperforms state-of-the-art for very low
resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions
The existing face image super-resolution (FSR) algorithms usually train a
specific model for a specific low input resolution for optimal results. By
contrast, we explore in this work a unified framework that is trained once and
then used to super-resolve input face images of varied low resolutions. For
that purpose, we propose a novel neural network architecture that is composed
of three anchor auto-encoders, one feature weight regressor and a final image
decoder. The three anchor auto-encoders are meant for optimal FSR for three
pre-defined low input resolutions, or named anchor resolutions, respectively.
An input face image of an arbitrary low resolution is firstly up-scaled to the
target resolution by bi-cubic interpolation and then fed to the three
auto-encoders in parallel. The three encoded anchor features are then fused
with weights determined by the feature weight regressor. At last, the fused
feature is sent to the final image decoder to derive the super-resolution
result. As shown by experiments, the proposed algorithm achieves robust and
state-of-the-art performance over a wide range of low input resolutions by a
single framework. Code and models will be made available after the publication
of this work
Face Restoration via Plug-and-Play 3D Facial Priors
State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g.,face super-resolution or deblurring). In this paper, we propose cross-tasks and cross-models plug-and-play 3D facial priors to explicitly embed the network with the sharp facial structures for general face restoration tasks. Our 3D priors are the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are very efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, for better exploiting this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content), a spatial attention module is designed for image restoration problems. Extensive face restoration experiments including face super-resolution and deblurring demonstrate that the proposed 3D priors achieve superior face restoration results over the state-of-the-art algorithm
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
We address the problem of restoring a high-resolution face image from a
blurry low-resolution input. This problem is difficult as super-resolution and
deblurring need to be tackled simultaneously. Moreover, existing algorithms
cannot handle face images well as low-resolution face images do not have much
texture which is especially critical for deblurring. In this paper, we propose
an effective algorithm by utilizing the domain-specific knowledge of human
faces to recover high-quality faces. We first propose a facial component guided
deep Convolutional Neural Network (CNN) to restore a coarse face image, which
is denoted as the base image where the facial component is automatically
generated from the input face image. However, the CNN based method cannot
handle image details well. We further develop a novel exemplar-based detail
enhancement algorithm via facial component matching. Extensive experiments show
that the proposed method outperforms the state-of-the-art algorithms both
quantitatively and qualitatively.Comment: In IJCV 201
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