40,377 research outputs found
Perspective-aware texture analysis and synthesis
The original publication is available at www.springerlink.comInternational audienceThis paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo
Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing
Free-viewpoint video conferencing allows a participant to observe the remote
3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint
image is commonly synthesized using two pairs of transmitted texture and depth
maps from two neighboring captured viewpoints via depth-image-based rendering
(DIBR). To maintain high quality of synthesized images, it is imperative to
contain the adverse effects of network packet losses that may arise during
texture and depth video transmission. Towards this end, we develop an
integrated approach that exploits the representation redundancy inherent in the
multiple streamed videos a voxel in the 3D scene visible to two captured views
is sampled and coded twice in the two views. In particular, at the receiver we
first develop an error concealment strategy that adaptively blends
corresponding pixels in the two captured views during DIBR, so that pixels from
the more reliable transmitted view are weighted more heavily. We then couple it
with a sender-side optimization of reference picture selection (RPS) during
real-time video coding, so that blocks containing samples of voxels that are
visible in both views are more error-resiliently coded in one view only, given
adaptive blending will erase errors in the other view. Further, synthesized
view distortion sensitivities to texture versus depth errors are analyzed, so
that relative importance of texture and depth code blocks can be computed for
system-wide RPS optimization. Experimental results show that the proposed
scheme can outperform the use of a traditional feedback channel by up to 0.82
dB on average at 8% packet loss rate, and by as much as 3 dB for particular
frames
Audio style transfer
'Style transfer' among images has recently emerged as a very active research
topic, fuelled by the power of convolution neural networks (CNNs), and has
become fast a very popular technology in social media. This paper investigates
the analogous problem in the audio domain: How to transfer the style of a
reference audio signal to a target audio content? We propose a flexible
framework for the task, which uses a sound texture model to extract statistics
characterizing the reference audio style, followed by an optimization-based
audio texture synthesis to modify the target content. In contrast to mainstream
optimization-based visual transfer method, the proposed process is initialized
by the target content instead of random noise and the optimized loss is only
about texture, not structure. These differences proved key for audio style
transfer in our experiments. In order to extract features of interest, we
investigate different architectures, whether pre-trained on other tasks, as
done in image style transfer, or engineered based on the human auditory system.
Experimental results on different types of audio signal confirm the potential
of the proposed approach.Comment: ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Apr 2018, Calgary, France. IEE
3D Face Synthesis Driven by Personality Impression
Synthesizing 3D faces that give certain personality impressions is commonly
needed in computer games, animations, and virtual world applications for
producing realistic virtual characters. In this paper, we propose a novel
approach to synthesize 3D faces based on personality impression for creating
virtual characters. Our approach consists of two major steps. In the first
step, we train classifiers using deep convolutional neural networks on a
dataset of images with personality impression annotations, which are capable of
predicting the personality impression of a face. In the second step, given a 3D
face and a desired personality impression type as user inputs, our approach
optimizes the facial details against the trained classifiers, so as to
synthesize a face which gives the desired personality impression. We
demonstrate our approach for synthesizing 3D faces giving desired personality
impressions on a variety of 3D face models. Perceptual studies show that the
perceived personality impressions of the synthesized faces agree with the
target personality impressions specified for synthesizing the faces. Please
refer to the supplementary materials for all results.Comment: 8pages;6 figure
Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture
This paper addresses the problem of interpolating visual textures. We
formulate this problem by requiring (1) by-example controllability and (2)
realistic and smooth interpolation among an arbitrary number of texture
samples. To solve it we propose a neural network trained simultaneously on a
reconstruction task and a generation task, which can project texture examples
onto a latent space where they can be linearly interpolated and projected back
onto the image domain, thus ensuring both intuitive control and realistic
results. We show our method outperforms a number of baselines according to a
comprehensive suite of metrics as well as a user study. We further show several
applications based on our technique, which include texture brush, texture
dissolve, and animal hybridization.Comment: Accepted to CVPR'1
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