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  Combining 3D Flow Fields with Silhouette-based Human Motion Capture for Immersive Video

Theobalt, C., Carranza, J., Magnor, M., & Seidel, H.-P. (2004). Combining 3D Flow Fields with Silhouette-based Human Motion Capture for Immersive Video. Graphical Models, 66, 333-351.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-29CE-7 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-29CF-5
Genre: Journal Article

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 Creators:
Theobalt, Christian1, 2, Author           
Carranza, Joel1, 3, Author           
Magnor, Marcus3, Author           
Seidel, Hans-Peter1, Author           
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
2Programming Logics, MPI for Informatics, Max Planck Society, ou_40045              
3Graphics - Optics - Vision, MPI for Informatics, Max Planck Society, ou_1116549              

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 Abstract: \begin{abstract} 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 \mbox{3D} video reality, the whole pipeline spanning from scene acquisition over \mbox{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. \end{abstract}

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Language(s): eng - English
 Dates: 2005-01-142004
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 231360
Other: Local-ID: C125675300671F7B-4C0C1106C6521B65C1256F5B004A40E1-TheobaltGM2004
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Title: Graphical Models
Source Genre: Journal
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Pages: - Volume / Issue: 66 Sequence Number: - Start / End Page: 333 - 351 Identifier: ISSN: 1524-0703