A Statistical Model of Human Pose and Body Shape
A Statistical Model of Human Pose and Body Shape
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DOI：
10.1111/j.1467-8659.2009.01373.x
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发表时间：
2009-01-01
影响因子：
2.5
通讯作者：
Seidel, H. -P.
中科院分区：
文献类型：
--
作者：
Hasler, N.;Stoll, C.;Seidel, H. -P.
现实人物的生成和动画是当今媒体行业许多项目的重要组成部分。特别是游戏和特效行业严重依赖于逼真的人类动画。在这项工作中，介绍了一个统一的模型，描述了两个，人体姿势和身体形状，使我们能够准确地建模肌肉变形不仅作为姿势的函数，但也依赖于体质的主题。再加上模型生成任意人体形状的能力，它大大简化了高度逼真的角色动画的生成。基于学习的方法在对114名受试者进行的大约550次全身3D激光扫描上进行训练。使用非刚性变形技术执行扫描配准。然后，所获取的样本的旋转不变编码允许同时编码姿势和身体形状的统计模型的计算。最后，变形或生成网格根据几个约束，同时可以通过训练语义有意义的回归。
Generation and animation of realistic humans is an essential part of many projects in today's media industry. Especially, the games and special effects industry heavily depend on realistic human animation. In this work a unified model that describes both, human pose and body shape is introduced which allows us to accurately model muscle deformations not only as a function of pose but also dependent on the physique of the subject. Coupled with the model's ability to generate arbitrary human body shapes, it severely simplifies the generation of highly realistic character animations. A learning based approach is trained on approximately 550 full body 3D laser scans taken of 114 subjects. Scan registration is performed using a non-rigid deformation technique. Then, a rotation invariant encoding of the acquired exemplars permits the computation of a statistical model that simultaneously encodes pose and body shape. Finally, morphing or generating meshes according to several constraints simultaneously can be achieved by training semantically meaningful regressors.