Creating the photo-realistic version of people sketched portraits is useful to various entertainment purposes. Existing studies only generate portraits in the 2D plane with fixed views, making the results less vivid. In this paper, we present Stereoscopic Simplified Sketch-to-Portrait (SSSP), which explores the possibility of creating Stereoscopic 3D-aware portraits from simple contour sketches by involving 3D generative models. Our key insight is to design sketch-aware constraints that can fully exploit the prior knowledge of a tri-plane-based 3D-aware generative model. Specifically, our designed region-aware volume rendering strategy and global consistency constraint further enhance detail correspondences during sketch encoding. Moreover, in order to facilitate the usage of layman users, we propose a Contour-to-Sketch module with vector quantized representations, so that easily drawn contours can directly guide the generation of 3D portraits. Extensive comparisons show that our method generates high-quality results that match the sketch. Our usability study verifies that our system is greatly preferred by user.
翻译:创建人们素描肖像的摄影现实版对于各种娱乐目的都是有用的。 现有的研究只能在 2D 平面上生成具有固定视图的肖像, 使结果不那么生动。 在本文中, 我们展示了Stereoscopic Simplication Strach- to- Portrait (SSSP ), 该文件探索了通过3D 基因化模型从简单的等距草图中创建Stereoscopic 3D- aware肖像的可能性。 我们的关键洞察是设计素描认知限制, 这些限制能够充分利用基于三平面的 3D- 觉的基因化模型的先前知识。 具体地说, 我们设计的区域认知量转换战略和全球一致性限制进一步加强了素描编译过程中的详细对应。 此外, 为了便利使用外行用户, 我们提议了一个带有矢量分解图解图解的“ 孔到Sketch” 模块, 这样容易绘制的等距可以直接指导生成3D 。 广泛比较表明我们的方法产生与素组相匹配的高品质结果。 我们的系统是用户非常喜欢的。