We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space. We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual. Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set. We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome. We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.
翻译:我们引入了MyStle, 这是一种个人化的深层基因化, 之前受过一些个人镜头的训练。 MyStle 允许重建、 提升和编辑特定个人的图像, 这样输出就能够忠实于个人的关键面部特征。 鉴于一小套个人肖像( ~ 100), 我们调整一个经过训练的StyGAN 面部生成器的重量, 以形成一个本地的、 低维度的、 个性化的元件。 我们显示这个元件是一个个性化区域, 跨越与个人不同肖像相联的潜在代码。 此外, 我们展示了我们之前获得过个人个性化基因化基因化的图像, 并提出了一个统一的方法, 来将它应用于各种错误的图像增强问题, 比如: 油漆和超级分辨率, 以及语义化的编辑。 我们使用个性化的图像, 展示了我们之前掌握的关于个人定性评估预定结果的定量基准, 并展示了我们之前的定性和定性模型。