8 research outputs found
Stylizing Face Images via Multiple Exemplars
We address the problem of transferring the style of a headshot photo to face
images. Existing methods using a single exemplar lead to inaccurate results
when the exemplar does not contain sufficient stylized facial components for a
given photo. In this work, we propose an algorithm to stylize face images using
multiple exemplars containing different subjects in the same style. Patch
correspondences between an input photo and multiple exemplars are established
using a Markov Random Field (MRF), which enables accurate local energy transfer
via Laplacian stacks. As image patches from multiple exemplars are used, the
boundaries of facial components on the target image are inevitably
inconsistent. The artifacts are removed by a post-processing step using an
edge-preserving filter. Experimental results show that the proposed algorithm
consistently produces visually pleasing results.Comment: In CVIU 2017. Project Page:
http://www.cs.cityu.edu.hk/~yibisong/cviu17/index.htm
Improved Sketch-to-Photo Generation Using Filter Aided Generative Adversarial Network
Generating a photographic face image from given input sketch is most challenging task in computer vision. Mainly the sketches drawn by sketch artist used in human identification. Sketch to photo synthesis is very important applications in law enforcement as well as character design, educational training. In recent years Generative Adversarial Network (GAN) shows excellent performance on sketch to photo synthesis problem. Quality of hand drawn sketches affects the quality generated photo. It might be possible that while handling the hand drawn sketches, accidently by touching the user hand on pencil sketch or similar activities causes noise in given sketch. Likewise different styles like shading, darkness of pencil used by sketch artist may cause unnecessary noise in sketches. In recent year many sketches to photo synthesis methods are proposed, but they are mainly focused on network architecture to get better performance. In this paper we proposed Filter-aided GAN framework to remove such noise while synthesizing photo images from hand drawn sketches. Here we implement and compare different filtering methods with GAN. Quantitative and qualitative result shows that proposed Filter-aided GAN generate the photo images which are visually pleasant and closer to ground truth image
VITAL: VIsual Tracking via Adversarial Learning
The tracking-by-detection framework consists of two stages, i.e., drawing
samples around the target object in the first stage and classifying each sample
as the target object or as background in the second stage. The performance of
existing trackers using deep classification networks is limited by two aspects.
First, the positive samples in each frame are highly spatially overlapped, and
they fail to capture rich appearance variations. Second, there exists extreme
class imbalance between positive and negative samples. This paper presents the
VITAL algorithm to address these two problems via adversarial learning. To
augment positive samples, we use a generative network to randomly generate
masks, which are applied to adaptively dropout input features to capture a
variety of appearance changes. With the use of adversarial learning, our
network identifies the mask that maintains the most robust features of the
target objects over a long temporal span. In addition, to handle the issue of
class imbalance, we propose a high-order cost sensitive loss to decrease the
effect of easy negative samples to facilitate training the classification
network. Extensive experiments on benchmark datasets demonstrate that the
proposed tracker performs favorably against state-of-the-art approaches.Comment: Spotlight in CVPR 201
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