The Neural Painter: Multi-Turn Image Generation
arXiv:1806.06183
Abstract
In this work we combine two research threads from Vision/ Graphics and Natural Language Processing to formulate an image generation task conditioned on attributes in a multi-turn setting. By multiturn, we mean the image is generated in a series of steps of user-specified conditioning information. Our proposed approach is practically useful and offers insights into neural interpretability. We introduce a framework that includes a novel training algorithm as well as model improvements built for the multi-turn setting. We demonstrate that this framework generates a sequence of images that match the given conditioning information and that this task is useful for more detailed benchmarking and analysis of conditional image generation methods.
References in corpus (8)
- Conditional Generative Adversarial Nets
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Generative Adversarial Text to Image Synthesis
- Conditional Image Synthesis With Auxiliary Classifier GANs
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- DRAW: A Recurrent Neural Network For Image Generation
- Generating Videos with Scene Dynamics