We suggest a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend ideas from classic stochastic procedural texturing (Perlin noise) to learned, deep, non-linearities. Our model encodes all exemplars from a diverse set of textures without a need to be re-trained for each exemplar. Applications include texture interpolation, and learning 3D textures from 2D exemplars
Natural images can be viewed as patchworks of different textures, where the local image statistics i...
Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized i...
Image textures can easily be created using texture synthesis by example. However, creating procedura...
Here we introduce a new model of natural textures based on the feature spaces of convolutional neura...
We introduce a new model of natural textures based on the feature spaces of convolutional neural net...
Abstract. This paper presents a statistical model for textures that uses a non-negative decompositio...
Numerous methods have been proposed for probabilistic generative modelling of 3D objects. However, n...
AbstractTraditionally, texture perception has been studied using artificial textures made of random ...
Automatic generation of 3D visual content is a fundamental problem that sits at the intersection of ...
It is a long standing question how biological systems transform visual inputs to robustly infer high...
We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a part-aware ...
This paper presents a mathematical framework for visual learning that integrates two popular statist...
Natural images can be viewed as patchworks of different textures, where the local image statistics i...
International audienceWe introduce a novel semi-procedural approach that avoids drawbacks of procedu...
We evaluate the ability of the popular Field-of-Experts (FoE) to model structure in images. As a tes...
Natural images can be viewed as patchworks of different textures, where the local image statistics i...
Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized i...
Image textures can easily be created using texture synthesis by example. However, creating procedura...
Here we introduce a new model of natural textures based on the feature spaces of convolutional neura...
We introduce a new model of natural textures based on the feature spaces of convolutional neural net...
Abstract. This paper presents a statistical model for textures that uses a non-negative decompositio...
Numerous methods have been proposed for probabilistic generative modelling of 3D objects. However, n...
AbstractTraditionally, texture perception has been studied using artificial textures made of random ...
Automatic generation of 3D visual content is a fundamental problem that sits at the intersection of ...
It is a long standing question how biological systems transform visual inputs to robustly infer high...
We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a part-aware ...
This paper presents a mathematical framework for visual learning that integrates two popular statist...
Natural images can be viewed as patchworks of different textures, where the local image statistics i...
International audienceWe introduce a novel semi-procedural approach that avoids drawbacks of procedu...
We evaluate the ability of the popular Field-of-Experts (FoE) to model structure in images. As a tes...
Natural images can be viewed as patchworks of different textures, where the local image statistics i...
Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized i...
Image textures can easily be created using texture synthesis by example. However, creating procedura...
Add to ORKG