AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style

• URL: http://arxiv.org/abs/2103.13722v1
• Date: Thu, 25 Mar 2021 10:09:45 GMT
• Title: AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
• Authors: Stanislav Frolov, Avneesh Sharma, J\"orn Hees, Tushar Karayil, Federico Raue, Andreas Dengel
• Abstract summary: We propose a method for attribute controlled image synthesis from layout. We extend a state-of-the-art approach for layout-to-image generation to condition individual objects on attributes. Our results show that our method can successfully control the fine-grained details of individual objects when modelling complex scenes with multiple objects.
• Score: 5.912209564607099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conditional image synthesis from layout has recently attracted much interest. Previous approaches condition the generator on object locations as well as class labels but lack fine-grained control over the diverse appearance aspects of individual objects. Gaining control over the image generation process is fundamental to build practical applications with a user-friendly interface. In this paper, we propose a method for attribute controlled image synthesis from layout which allows to specify the appearance of individual objects without affecting the rest of the image. We extend a state-of-the-art approach for layout-to-image generation to additionally condition individual objects on attributes. We create and experiment on a synthetic, as well as the challenging Visual Genome dataset. Our qualitative and quantitative results show that our method can successfully control the fine-grained details of individual objects when modelling complex scenes with multiple objects.

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