Constrained Neural Style Transfer for Decorated Logo Generation
This addresses the need for automated logo creation tools to reduce time and skill requirements, though it appears incremental as it builds on existing neural style transfer techniques.
The paper tackles the problem of creating decorated logos by proposing a neural style transfer method that preserves silhouettes of text and objects using a new distance transform-based loss function, demonstrating logo generation with various inputs.
Making decorated logos requires image editing skills, without sufficient skills, it could be a time-consuming task. While there are many on-line web services to make new logos, they have limited designs and duplicates can be made. We propose using neural style transfer with clip art and text for the creation of new and genuine logos. We introduce a new loss function based on distance transform of the input image, which allows the preservation of the silhouettes of text and objects. The proposed method constrains style transfer only around the designated area. We demonstrate the characteristics of proposed method. Finally, we show the results of logo generation with various input images.