| Popis: |
We present image generation networks to generate images conforming to specified weather attributes. Taking weather attributes as the conditions, the proposed networks generate scene images with the help of a guided reference image. To generate higher-resolution images, we construct a multi-scale generation framework consisting of a global generator and a local enhancer. Furthermore, we integrate the idea of residual learning into the proposed framework, and aim at generating fine-grained texture. The evaluation shows performance comparison both from quantitative and qualitative perspectives. A comprehensive study including the impact of different attributes and extension of the proposed models is also provided. This work is kind of hybrid approach among various image generation studies. |
