Nov. 17, 2022, 2:15 a.m. | Xiang Yuejia, Lv Chuanhao, Liu Qingdazhu, Yang Xiaocui, Liu Bo, Ju Meizhi

cs.CV updates on arXiv.org arxiv.org

Most image generation methods are difficult to precisely control the
properties of the generated images, such as structure, scale, shape, etc.,
which limits its large-scale application in creative industries such as
conceptual design and graphic design, and so on. Using the prompt and the
sketch is a practical solution for controllability. Existing datasets lack
either prompt or sketch and are not designed for the creative industry. Here is
the main contribution of our work. a) This is the first dataset …

arxiv creative dataset image image generation industry

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Staff Software Engineer, Generative AI, Google Cloud AI

@ Google | Mountain View, CA, USA; Sunnyvale, CA, USA

Expert Data Sciences

@ Gainwell Technologies | Any city, CO, US, 99999