BachGAN: High-Resolution Image Synthesis from Salient Object Layout
arXiv:2003.11690
Abstract
We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout. This new setting allows users to provide the layout of salient objects only (i.e., foreground bounding boxes and categories), and lets the model complete the drawing with an invented background and a matching foreground. Two main challenges spring from this new task: (i) how to generate fine-grained details and realistic textures without segmentation map input; and (ii) how to create a background and weave it seamlessly into standalone objects. To tackle this, we propose Background Hallucination Generative Adversarial Network (BachGAN), which first selects a set of segmentation maps from a large candidate pool via a background retrieval module, then encodes these candidate layouts via a background fusion module to hallucinate a suitable background for the given objects. By generating the hallucinated background representation dynamically, our model can synthesize high-resolution images with both photo-realistic foreground and integral background. Experiments on Cityscapes and ADE20K datasets demonstrate the advantage of BachGAN over existing methods, measured on both visual fidelity of generated images and visual alignment between output images and input layouts.
arXiv commentAccepted to CVPR 2020
References in corpus (7)
- Auto-Encoding Variational Bayes
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Conditional Image Generation with PixelCNN Decoders
- Pixel Recurrent Neural Networks
- Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
- Context-Aware Synthesis and Placement of Object Instances
- TextureGAN: Controlling Deep Image Synthesis with Texture Patches