23,604 research outputs found
Multiface: A Dataset for Neural Face Rendering
Photorealistic avatars of human faces have come a long way in recent years,
yet research along this area is limited by a lack of publicly available,
high-quality datasets covering both, dense multi-view camera captures, and rich
facial expressions of the captured subjects. In this work, we present
Multiface, a new multi-view, high-resolution human face dataset collected from
13 identities at Reality Labs Research for neural face rendering. We introduce
Mugsy, a large scale multi-camera apparatus to capture high-resolution
synchronized videos of a facial performance. The goal of Multiface is to close
the gap in accessibility to high quality data in the academic community and to
enable research in VR telepresence. Along with the release of the dataset, we
conduct ablation studies on the influence of different model architectures
toward the model's interpolation capacity of novel viewpoint and expressions.
With a conditional VAE model serving as our baseline, we found that adding
spatial bias, texture warp field, and residual connections improves performance
on novel view synthesis. Our code and data is available at:
https://github.com/facebookresearch/multifac
A survey of exemplar-based texture synthesis
Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe
FRAME. New method presented: CNNMR
Contextual-based Image Inpainting: Infer, Match, and Translate
We study the task of image inpainting, which is to fill in the missing region
of an incomplete image with plausible contents. To this end, we propose a
learning-based approach to generate visually coherent completion given a
high-resolution image with missing components. In order to overcome the
difficulty to directly learn the distribution of high-dimensional image data,
we divide the task into inference and translation as two separate steps and
model each step with a deep neural network. We also use simple heuristics to
guide the propagation of local textures from the boundary to the hole. We show
that, by using such techniques, inpainting reduces to the problem of learning
two image-feature translation functions in much smaller space and hence easier
to train. We evaluate our method on several public datasets and show that we
generate results of better visual quality than previous state-of-the-art
methods.Comment: ECCV 2018 camera read
Controlling Perceptual Factors in Neural Style Transfer
Neural Style Transfer has shown very exciting results enabling new forms of
image manipulation. Here we extend the existing method to introduce control
over spatial location, colour information and across spatial scale. We
demonstrate how this enhances the method by allowing high-resolution controlled
stylisation and helps to alleviate common failure cases such as applying ground
textures to sky regions. Furthermore, by decomposing style into these
perceptual factors we enable the combination of style information from multiple
sources to generate new, perceptually appealing styles from existing ones. We
also describe how these methods can be used to more efficiently produce large
size, high-quality stylisation. Finally we show how the introduced control
measures can be applied in recent methods for Fast Neural Style Transfer.Comment: Accepted at CVPR201
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.Comment: Accepted at ECCV 201
- …