7,919 research outputs found
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing
We address the problem of finding realistic geometric corrections to a
foreground object such that it appears natural when composited into a
background image. To achieve this, we propose a novel Generative Adversarial
Network (GAN) architecture that utilizes Spatial Transformer Networks (STNs) as
the generator, which we call Spatial Transformer GANs (ST-GANs). ST-GANs seek
image realism by operating in the geometric warp parameter space. In
particular, we exploit an iterative STN warping scheme and propose a sequential
training strategy that achieves better results compared to naive training of a
single generator. One of the key advantages of ST-GAN is its applicability to
high-resolution images indirectly since the predicted warp parameters are
transferable between reference frames. We demonstrate our approach in two
applications: (1) visualizing how indoor furniture (e.g. from product images)
might be perceived in a room, (2) hallucinating how accessories like glasses
would look when matched with real portraits.Comment: Accepted to CVPR 2018 (website & code:
https://chenhsuanlin.bitbucket.io/spatial-transformer-GAN/
Multilinear Wavelets: A Statistical Shape Space for Human Faces
We present a statistical model for D human faces in varying expression,
which decomposes the surface of the face using a wavelet transform, and learns
many localized, decorrelated multilinear models on the resulting coefficients.
Using this model we are able to reconstruct faces from noisy and occluded D
face scans, and facial motion sequences. Accurate reconstruction of face shape
is important for applications such as tele-presence and gaming. The localized
and multi-scale nature of our model allows for recovery of fine-scale detail
while retaining robustness to severe noise and occlusion, and is
computationally efficient and scalable. We validate these properties
experimentally on challenging data in the form of static scans and motion
sequences. We show that in comparison to a global multilinear model, our model
better preserves fine detail and is computationally faster, while in comparison
to a localized PCA model, our model better handles variation in expression, is
faster, and allows us to fix identity parameters for a given subject.Comment: 10 pages, 7 figures; accepted to ECCV 201
Side-View Face Recognition
Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
3D Morphable Model (3DMM) based methods have achieved great success in
recovering 3D face shapes from single-view images. However, the facial textures
recovered by such methods lack the fidelity as exhibited in the input images.
Recent work demonstrates high-quality facial texture recovering with generative
networks trained from a large-scale database of high-resolution UV maps of face
textures, which is hard to prepare and not publicly available. In this paper,
we introduce a method to reconstruct 3D facial shapes with high-fidelity
textures from single-view images in-the-wild, without the need to capture a
large-scale face texture database. The main idea is to refine the initial
texture generated by a 3DMM based method with facial details from the input
image. To this end, we propose to use graph convolutional networks to
reconstruct the detailed colors for the mesh vertices instead of reconstructing
the UV map. Experiments show that our method can generate high-quality results
and outperforms state-of-the-art methods in both qualitative and quantitative
comparisons.Comment: Accepted to CVPR 2020. The source code is available at
https://github.com/FuxiCV/3D-Face-GCN
Pose Invariant 3D Face Authentication based on Gaussian Fields Approach
This thesis presents a novel illuminant invariant approach to recognize the identity of an individual from his 3D facial scan in any pose, by matching it with a set of frontal models stored in the gallery. In view of todayās security concerns, 3D face reconstruction and recognition has gained a significant position in computer vision research. The non intrusive nature of facial data acquisition makes face recognition one of the most popular approaches for biometrics-based identity recognition. Depth information of a 3D face can be used to solve the problems of illumination and pose variation associated with face recognition.
The proposed method makes use of 3D geometric (point sets) face representations for recognizing faces. The use of 3D point sets to represent human faces in lieu of 2D texture makes this method robust to changes in illumination and pose. The method first automatically registers facial point-sets of the probe with the gallery models through a criterion based on Gaussian force fields. The registration method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. The new method overcomes the necessity of close initialization and converges in much less iterations as compared to the Iterative Closest Point algorithm. The use of an optimization method, the Fast Gauss Transform, allows a considerable reduction in the computational complexity of the registration algorithm. Recognition is then performed by using the robust similarity score generated by registering 3D point sets of faces. Our approach has been tested on a large database of 85 individuals with 521 scans at different poses, where the gallery and the probe images have been acquired at significantly different times. The results show the potential of our approach toward a fully pose and illumination invariant system. Our method can be successfully used as a potential biometric system in various applications such as mug shot matching, user verification and access control, and enhanced human computer interaction
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
- ā¦