论文标题
IDE-3D：高分辨率3D感知肖像合成的交互式解开编辑
IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis
论文作者
论文摘要
现有的3D感知面部生成方法面临着质量与编辑性的困境：它们可以产生可编辑的结果低分辨率或没有编辑灵活性的高质量的结果。在这项工作中，我们提出了一种新的方法，将两全其美的最好的方法融合在一起。我们的系统由三个主要组成部分组成：（1）一个3D-semantics-taw的生成模型，可产生视图一致的，不阐明的面部图像和语义面具； （2）一种混合GAN反转方法，该方法将语义和纹理编码器的潜在代码初始化，并进一步优化它们以进行忠实的重建； （3）在规范视图和产品高质量编辑结果中有效地操纵语义面具的规范编辑器。我们的方法有能力用于许多应用程序，例如自由观看面图，编辑和样式控制。定量和定性结果都表明，我们的方法在光真相，忠诚和效率方面达到了最先进的结果。
Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initialize the latent codes from the semantic and texture encoder, and further optimized them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and product high-quality editing results. Our approach is competent for many applications, e.g. free-view face drawing, editing, and style control. Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness, and efficiency.
