Creatism: A deep-learning photographer capable of creating professional work. Fang, H. & Zhang, M. arXiv:1707.03491 [cs], July, 2017. arXiv: 1707.03491![Creatism: A deep-learning photographer capable of creating professional work [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Machine-learning excels in many areas with well-defined goals. However, a clear goal is usually not available in art forms, such as photography. The success of a photograph is measured by its aesthetic value, a very subjective concept. This adds to the challenge for a machine learning approach. We introduce Creatism, a deep-learning system for artistic content creation. In our system, we break down aesthetics into multiple aspects, each can be learned individually from a shared dataset of professional examples. Each aspect corresponds to an image operation that can be optimized efficiently. A novel editing tool, dramatic mask, is introduced as one operation that improves dramatic lighting for a photo. Our training does not require a dataset with before/after image pairs, or any additional labels to indicate different aspects in aesthetics. Using our system, we mimic the workflow of a landscape photographer, from framing for the best composition to carrying out various post-processing operations. The environment for our virtual photographer is simulated by a collection of panorama images from Google Street View. We design a "Turing-test"-like experiment to objectively measure quality of its creations, where professional photographers rate a mixture of photographs from different sources blindly. Experiments show that a portion of our robot's creation can be confused with professional work.
@article{fang_creatism:_2017,
title = {Creatism: {A} deep-learning photographer capable of creating professional work},
shorttitle = {Creatism},
url = {http://arxiv.org/abs/1707.03491},
abstract = {Machine-learning excels in many areas with well-defined goals. However, a clear goal is usually not available in art forms, such as photography. The success of a photograph is measured by its aesthetic value, a very subjective concept. This adds to the challenge for a machine learning approach. We introduce Creatism, a deep-learning system for artistic content creation. In our system, we break down aesthetics into multiple aspects, each can be learned individually from a shared dataset of professional examples. Each aspect corresponds to an image operation that can be optimized efficiently. A novel editing tool, dramatic mask, is introduced as one operation that improves dramatic lighting for a photo. Our training does not require a dataset with before/after image pairs, or any additional labels to indicate different aspects in aesthetics. Using our system, we mimic the workflow of a landscape photographer, from framing for the best composition to carrying out various post-processing operations. The environment for our virtual photographer is simulated by a collection of panorama images from Google Street View. We design a "Turing-test"-like experiment to objectively measure quality of its creations, where professional photographers rate a mixture of photographs from different sources blindly. Experiments show that a portion of our robot's creation can be confused with professional work.},
urldate = {2018-01-17TZ},
journal = {arXiv:1707.03491 [cs]},
author = {Fang, Hui and Zhang, Meng},
month = jul,
year = {2017},
note = {arXiv: 1707.03491},
keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
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