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Combining 3D flow fields with silhouette-based human motion capture for immersive video

机译：将3D流场与基于轮廓的人体运动捕捉相结合，以实现沉浸式视频

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摘要

In recent years, the convergence of computer vision and computer graphics has put forth a new field of research that focuses on the reconstruction of real-world scenes from video streams. To make immersive 3D video reality, the whole pipeline spanning from scene acquisition over 3D video reconstruction to real-time rendering needs to be researched. In this paper, we describe latest advancements of our system to record, reconstruct and render free-viewpoint videos of human actors. We apply a silhouette-based non-intrusive motion capture algorithm making use of a 3D human body model to estimate the actor's parameters of motion from multi-view video streams. A renderer plays back the acquired motion sequence in real-time from any arbitrary perspective. Photo-realistic physical appearance of the moving actor is obtained by generating time-varying multi-view textures from video. This work shows how the motion capture sub-system can be enhanced by incorporating texture information from the input video streams into the tracking process. 3D motion fields are reconstructed from optical flow that are used in combination with silhouette matching to estimate pose parameters. We demonstrate that a high visual quality can be achieved with the proposed approach and validate the enhancements caused by the the motion field step.
机译：近年来，计算机视觉和计算机图形学的融合提出了一个新的研究领域，其重点是从视频流重构真实世界的场景。为了使沉浸式3D视频成为现实，需要研究从场景采集到3D视频重建再到实时渲染的整个流程。在本文中，我们描述了用于记录，重建和渲染人类演员的自由视点视频的系统的最新进展。我们应用了基于轮廓的非侵入式运动捕获算法，该算法利用3D人体模型从多视图视频流中估计演员的运动参数。渲染器从任意角度实时回放获取的运动序列。通过从视频生成时变的多视图纹理来获得移动演员的逼真的物理外观。这项工作显示了如何通过将来自输入视频流的纹理信息合并到跟踪过程中来增强运动捕获子系统。从光流中重建3D运动场，将其与轮廓匹配结合使用以估计姿势参数。我们证明了通过所提出的方法可以实现较高的视觉质量，并验证了运动场步骤引起的增强效果。

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