基于笼子的性能捕获

Yann Savoye
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引用次数: 5

摘要

如今,演员真人表演的高细节动画越来越容易获得,3D视频在视觉媒体制作中得到了相当大的关注。本讲座将解决新的范例,以实现性能捕获使用笼子为基础的形状在运动。我们将基于笼子的性能捕获定义为以稀疏控制变形处理轨迹和激光扫描静态模板形状的形式从多视图捕获演员的非刚性表面的非侵入性过程。在本课程中,我们通过四个步骤解决提取或获取然后重用基于视频的动画的非刚性参数化的难题:(1)基于笼子的逆运动学,(2)将表面性能捕获转换为基于笼子的变形,(3)基于笼子的卡通表面夸张,以及(4)基于笼子的时变重构点云的配准。关键目标是吸引游戏程序员,数字艺术家和电影制作人的兴趣,使用纯粹的几何和动画友好的工具来捕获和重用运动中的表面。最后,各种先进的动画技术和基于视觉的图形应用程序可以从本课程中提出的基于动画坐标的子空间中受益。乍一看,一个关键的挑战是在保留有限数量的可控、灵活和可重复使用参数的动态表面的全局和局部捕获特性的同时,再现可信的无骨变形。在放弃经典的关节骨架作为底层结构的同时,我们展示了基于笼子的变形器通过学习时空形状的可变性,为动态非刚性表面运动提供了灵活的设计空间抽象。注册的笼柄轨迹允许通过变形封闭的精细细节网格来重建复杂的网格序列。最后,基于笼子的性能捕获技术通过将运动与几何解耦,为动画传输提供了合适且可重用的输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cage-based performance capture
Nowadays, highly-detailed animations of live-actor performances are increasingly easier to acquire, and 3D Video has reached considerable attention in visual media productions. This lecture will address new paradigm to achieve performance capture using cage-based shapes in motion. We define cage-based performance capture as the non-invasive process of capturing non-rigid surface of actors from multi-view in the form of sparse control deformation handles trajectories and a laser-scanned static template shape. In this course, we address the hard problem of extracting or acquiring and then reusing non-rigid parametrization for video-based animations in four steps: (1) cage-based inverse kinematics, (2) conversion of surface performance capture into cage-based deformation, (3) cage-based cartoon surface exaggeration, and (4) cage-based registration of time-varying reconstructed point clouds. The key objective is to attract the interest of game programmers, digital artists and filmmakers in employing purely geometric and animator-friendly tools to capture and reuse surfaces in motion. Finally, a variety of advanced animation techniques and vision-based graphics applications could benefit from animatable coordinates-based sub-spaces as presented in this course. At first sight, a crucial challenge is to reproduce plausible boneless deformations while preserving global and local captured properties of dynamic surfaces with a limited number of controllable, flexible and reusable parameters. While abandoning the classical articulated skeleton as the underlying structure, we show that cage-based deformers offer a flexible design space abstraction to dynamically non-rigid surface motion through learning space-time shape variability. Registered cage-handles trajectories allow the reconstruction of complex mesh sequences by deforming an enclosed fine-detail mesh. Finally, cage-based performance capture techniques offer suitable and reusable outputs for animation transfer by decoupling the motion from the geometry.
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