DIFAI: Diverse Facial Inpainting using StyleGAN Inversion

Abstract

Image inpainting is an old problem in computer vision that restores occluded regions and completes damaged images. In the case of facial image inpainting, most of the methods generate only one result for each masked image, even though there are other reasonable possibilities. To prevent any potential biases and unnatural constraints stemming from generating only one image, we propose a novel framework for diverse facial inpainting exploiting the embedding space of StyleGAN. Our framework employs pSp encoder and SeFa algorithm to identify semantic components of the StyleGAN embeddings and feed them into our proposed SPARN decoder that adopts region normalization for plausible inpainting. We demonstrate that our proposed method outperforms several state-of-the-art methods.

TolerantGAN: Text-guided Image Manipulation Tolerant to Real-world Image

“…As applications that effectively leverage the power of spaces W and W+, several techniques such as image inpainting [9], [10], style transfer [11], [12], and the full-body human generation and manipulation [13], [14], have gained significant attention in recent years. These techniques are primarily developed to tackle specific tasks.…”