38 research outputs found
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.e., always produce visually identical output) and suboptimality
(i.e., generate less satisfying visual effects). In this work, we focus on
solving these issues for improved texture synthesis. We propose a deep
generative feed-forward network which enables efficient synthesis of multiple
textures within one single network and meaningful interpolation between them.
Meanwhile, a suite of important techniques are introduced to achieve better
convergence and diversity. With extensive experiments, we demonstrate the
effectiveness of the proposed model and techniques for synthesizing a large
number of textures and show its applications with the stylization.Comment: accepted by CVPR201
Approximation of tensor fields on surfaces of arbitrary topology based on local Monge parametrizations
We introduce a new method, the Local Monge Parametrizations (LMP) method, to
approximate tensor fields on general surfaces given by a collection of local
parametrizations, e.g.~as in finite element or NURBS surface representations.
Our goal is to use this method to solve numerically tensor-valued partial
differential equations (PDE) on surfaces. Previous methods use scalar
potentials to numerically describe vector fields on surfaces, at the expense of
requiring higher-order derivatives of the approximated fields and limited to
simply connected surfaces, or represent tangential tensor fields as tensor
fields in 3D subjected to constraints, thus increasing the essential number of
degrees of freedom. In contrast, the LMP method uses an optimal number of
degrees of freedom to represent a tensor, is general with regards to the
topology of the surface, and does not increase the order of the PDEs governing
the tensor fields. The main idea is to construct maps between the element
parametrizations and a local Monge parametrization around each node. We test
the LMP method by approximating in a least-squares sense different vector and
tensor fields on simply connected and genus-1 surfaces. Furthermore, we apply
the LMP method to two physical models on surfaces, involving a tension-driven
flow (vector-valued PDE) and nematic ordering (tensor-valued PDE). The LMP
method thus solves the long-standing problem of the interpolation of tensors on
general surfaces with an optimal number of degrees of freedom.Comment: 16 pages, 6 figure
Texture Synthesis for Mobile Data Communications
A digital camera mounted on a mobile phone is utilized as a data input device to obtain embedded data by analyzing the pattern of an image code such as a 2D bar code. This article proposes a new type of image coding method using texture image synthesis. Regularly arranged dotted-pattern is first painted with colors picked out from a texture sample, for having features corresponding to embedded data. Our texture synthesis technique then camouflages the dotted-patternusing the same texture sample while preserving the qualitycomparable to that of existing synthesis techniques. The texturedcode provides the conventional bar code with an aesthetic appealand is used for tagging data onto real texture objects, which canform a basis for ubiquitous mobile data communications. Thistechnical approach has the potential to explore new applicationfields of example-based, computer-generated texture images
Using Texture Synthesis for Non-Photorealistic Shading from Paint Samples
This paper presents several methods for shading meshes from scanned paint samples that represent dark to light transitions. Our techniques emphasize artistic control of brush stroke texture and color. We first demonstrate how the texture of the paint sample can be separated from its color gradient. We demonstrate three methods, two real-time and one off-line for producing rendered, shaded images from the texture samples. All three techniques use texture synthesis to generate additional paint samples. Finally, we develop metrics for evaluating how well each method achieves our goal in terms of texture similarity, shading correctness and temporal coherence