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In recent years, text-guided image manipulation has gained increasing attention in the image generation research field. Recent works have proposed to deal with a simplified setting where the input image only has a single object and the text modification is acquired by swapping image captions or labels. In this paper, we study a setting that allows users to edit an image with multiple objects using complex text instructions. In this image generation task, the inputs are a reference image and an instruction in natural language that describes desired modifications to the input image. We propose a GAN-based method to tackle this problem. The key idea is to treat text as neural operators to locally modify the image feature. We show that the proposed model performs favorably against recent baselines on three public datasets.
Image inpainting task requires filling the corrupted image with contents coherent with the context. This research field has achieved promising progress by using neural image inpainting methods. Nevertheless, there is still a critical challenge in gue
We propose a novel lightweight generative adversarial network for efficient image manipulation using natural language descriptions. To achieve this, a new word-level discriminator is proposed, which provides the generator with fine-grained training f
In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and i
The existing text-guided image synthesis methods can only produce limited quality results with at most mbox{$text{256}^2$} resolution and the textual instructions are constrained in a small Corpus. In this work, we propose a unified framework for bot
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images. However, discovering sema