4,302 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
Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning
Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL
Facial Point Detection using Boosted Regression and Graph Models
Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a pointās location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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