5,612 research outputs found
High quality solid texture synthesis using position and index histogram matching
International audienceThe synthesis quality is one of the most important aspects in solid texture synthesis algorithms. In recent years several methods are proposed to generate high quality solid textures. However, these existing methods often suffer from the synthesis artifacts such as blurring, missing texture structures, introducing aberrant voxel colors, and so on. In this paper, we introduce a novel algorithm for synthesizing high quality solid textures from 2D exemplars. We first analyze the relevant factors for further improvements of the synthesis quality, and then adopt an optimization framework with the k-coherence search and the discrete solver for solid texture synthesis. The texture optimization approach is integrated with two new kinds of histogram matching methods, position and index histogram matching, which effectively cause the global statistics of the synthesized solid textures to match those of the exemplars. Experimental results show that our algorithm outperforms or at least is comparable to the previous solid texture synthesis algorithms in terms of the synthesis quality
Non-parametric synthesis of laminar volumetric texture
International audienceThe goal of this paper is to evaluate several extensions of Wei and Levoy's algorithm for the synthesis of laminar volumetric textures constrained only by a single 2D sample. Hence, we shall also review in a unified form the improved algorithm proposed by Kopf et al. and the particular histogram matching approach of Chen and Wang. Developing a genuine quantitative study we are able to compare the performances of these algorithms that we have applied to the synthesis of volumetric structures of dense carbons. The 2D samples are lattice fringe images obtained by high resolution transmission electronic microscopy (HRTEM)
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
Solid Texture Synthesis using Generative Adversarial Networks
Solid texture synthesis, as an effective way to extend 2D exemplar to a
volumetric texture, exhibits advantages in numerous application domains.
However, existing methods generally suffer from synthesis distortion due to the
under-utilization of information. In this paper, we propose a novel approach
for the solid texture synthesis based on generative adversarial networks(GANs),
named STS-GAN, learning the distribution of 2D exemplars with volumetric
operation in a feature-free manner. The multi-scale discriminators evaluate the
similarities between patch exemplars and slices from generated volume,
promoting the generator to synthesize realistic solid texture. Experimental
results demonstrate that the proposed method can synthesize high-quality solid
texture with similar visual characteristics to the exemplar
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
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