Co-occurrence based texture synthesis
نویسندگان
چکیده
Abstract As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive manipulate. In this work, we turn co-occurrence statistics, which have long been used for texture analysis, learn controllable synthesis model. We propose fully convolutional generative adversarial network, conditioned locally on generate arbitrarily large images while having local, interpretable control over appearance. To encourage fidelity the input condition, introduce novel differentiable loss integrated seamlessly into our framework an end-to-end fashion. demonstrate solution offers stable, intuitive, latent representation synthesis, can be smooth morphs between different textures. further show interactive tool allows user adjust local characteristics of synthesized by directly using values.
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ژورنال
عنوان ژورنال: Computational Visual Media
سال: 2021
ISSN: ['2096-0662', '2096-0433']
DOI: https://doi.org/10.1007/s41095-021-0243-7