论文标题
人体模型姿势的互动素描
Interactive Sketching of Mannequin Poses
论文作者
论文摘要
用不同的姿势绘制人类绘制人类可能很容易。相比之下，在3D图形“ Mannequin”上创建相同的姿势是相对乏味的。然而，3D身体姿势对于各种下游应用是必需的。我们试图保留2D草图的便利性，同时为不同技能水平的用户提供灵活性，以准确，更快地摆姿势\ slash精炼3D模特。 在交互式系统的核心中，我们提出了一个机器学习模型，用于从以圆柱体式风格绘制的人类草图中推断出CG模特的3D姿势。训练这种模型由于艺术家的可变性，缺乏相应地面真相3D姿势的草图训练数据以及人姿势空间的高维度而具有挑战性。我们综合矢量图形训练数据的独特方法为我们的集成ML和基因模式系统的基础。除定量比较外，我们还通过将系统与用户界面紧密耦合并进行用户研究来验证系统。
It can be easy and even fun to sketch humans in different poses. In contrast, creating those same poses on a 3D graphics "mannequin" is comparatively tedious. Yet 3D body poses are necessary for various downstream applications. We seek to preserve the convenience of 2D sketching while giving users of different skill levels the flexibility to accurately and more quickly pose\slash refine a 3D mannequin. At the core of the interactive system, we propose a machine-learning model for inferring the 3D pose of a CG mannequin from sketches of humans drawn in a cylinder-person style. Training such a model is challenging because of artist variability, a lack of sketch training data with corresponding ground truth 3D poses, and the high dimensionality of human pose-space. Our unique approach to synthesizing vector graphics training data underpins our integrated ML-and-kinematics system. We validate the system by tightly coupling it with a user interface, and by performing a user study, in addition to quantitative comparisons.
