Abstract
Assisting people in efficiently producing visually plausible 3D characters has always been a fundamental research topic in computer vision and computer graphics. Recent learning-based approaches have achieved unprecedented accuracy and efficiency in the area of 3D real human digitization. However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming. In this paper, we introduce 3DBiCar, the first large-scale dataset of 3D biped cartoon characters, and RaBit, the corresponding parametric model. Our dataset contains 1,500 topologically consistent high-quality 3D textured models which are manually crafted by professional artists. Built upon the data, RaBit is thus designed with a SMPL-like linear blend shape model and a StyleGAN-based neural UV-texture generator, simultaneously expressing the shape, pose, and texture. To demonstrate the practicality of 3DBiCar and RaBit, various applications are conducted, including single-view reconstruction, sketch-based modeling, and 3D cartoon animation. For the single-view reconstruction setting, we find a straightforward global mapping from input images to the output UV-based texture maps tends to lose detailed appearances of some local parts (e.g., nose, ears). Thus, a part-sensitive texture reasoner is adopted to make all important local areas perceived. Experiments further demonstrate the effectiveness of our method both qualitatively and quantitatively. 3DBiCar and RaBit are available at this http URL.
Abstract (translated)
协助人们高效制造视觉效果合理的三维角色一直是计算机视觉和计算机图形领域的基本研究话题。近年来,基于学习的方法在3D真实人类数字化领域实现了前所未有的准确性和效率。然而,以前的工作并没有专注于建模三维双足卡通角色,这些角色在游戏和电影拍摄中也非常受欢迎。在本文中,我们介绍了3DBiCar,这是第一个大规模3D双足卡通角色数据集,以及RaBit,它的参数化模型。我们的数据集包含了由专业艺术家手动制作的具有正确拓扑结构的高质量3D纹理模型1500个,基于这些数据,RaBit采用类似于SMPL的线性混合形状模型和基于风格GAN的 UV纹理生成神经网络,同时表达形状、姿态和纹理。为了展示3DBiCar和RaBit的实际可行性,我们进行了多种应用测试,包括单视角重建、基于 Sketch 的建模和3D卡通动画。对于单视角重建设置,我们发现从输入图像到输出 UV 纹理映射的直观全球映射倾向于失去一些局部部件(如鼻子和耳朵)的详细外观。因此,采用了局部纹理敏感的原因分析器,以便所有重要局部区域都被感知到。实验还证明了我们方法的定性和定量效果。3DBiCar和RaBit可在本网站的http://URL上获取。
URL
https://arxiv.org/abs/2303.12564
