Luo Zhongjin, Du Dong, Zhu Heming, Yu Yizhou, Fu Hongbo, Han Xiaoguang
IEEE Trans Vis Comput Graph. 2024 Aug;30(8):5260-5275. doi: 10.1109/TVCG.2023.3291703. Epub 2024 Jul 1.
Modeling 3D avatars benefits various application scenarios such as AR/VR, gaming, and filming. Character faces contribute significant diversity and vividity as a vital component of avatars. However, building 3D character face models usually requires a heavy workload with commercial tools, even for experienced artists. Various existing sketch-based tools fail to support amateurs in modeling diverse facial shapes and rich geometric details. In this article, we present SketchMetaFace - a sketching system targeting amateur users to model high-fidelity 3D faces in minutes. We carefully design both the user interface and the underlying algorithm. First, curvature-aware strokes are adopted to better support the controllability of carving facial details. Second, considering the key problem of mapping a 2D sketch map to a 3D model, we develop a novel learning-based method termed "Implicit and Depth Guided Mesh Modeling" (IDGMM). It fuses the advantages of mesh, implicit, and depth representations to achieve high-quality results with high efficiency. In addition, to further support usability, we present a coarse-to-fine 2D sketching interface design and a data-driven stroke suggestion tool. User studies demonstrate the superiority of our system over existing modeling tools in terms of the ease to use and visual quality of results. Experimental analyses also show that IDGMM reaches a better trade-off between accuracy and efficiency.
3D 虚拟形象建模有利于各种应用场景,如增强现实/虚拟现实、游戏和电影制作。角色面部作为虚拟形象的重要组成部分,赋予了显著的多样性和生动性。然而,使用商业工具构建 3D 角色面部模型通常需要大量的工作量,即使对于经验丰富的艺术家也是如此。现有的各种基于草图的工具无法支持业余爱好者对多样的面部形状和丰富的几何细节进行建模。在本文中,我们展示了 SketchMetaFace——一种面向业余用户的草图绘制系统,可在数分钟内对高保真 3D 面部进行建模。我们精心设计了用户界面和底层算法。首先,采用曲率感知笔触以更好地支持雕刻面部细节的可控性。其次,考虑到将 2D 草图映射到 3D 模型的关键问题,我们开发了一种名为“隐式和深度引导网格建模”(IDGMM)的基于学习的新方法。它融合了网格、隐式和深度表示的优点,以高效实现高质量的结果。此外,为了进一步支持可用性,我们提出了一种从粗到细的 2D 草图绘制界面设计和一个数据驱动的笔触建议工具。用户研究证明了我们的系统在易用性和结果视觉质量方面优于现有建模工具。实验分析还表明,IDGMM 在准确性和效率之间达到了更好的平衡。