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Content Provider | IET Digital Library |
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Author | Hilsmann, Anna Fechteler, Philipp Morgenstern, Wieland Paier, Wolfgang Feldmann, Ingo Schreer, Oliver Eisert, Peter |
Abstract | An end-to-end pipeline for the creation of high-quality animatable volumetric video of human performances is presented. Going beyond the application of free-viewpoint video, the authors allow re-animation and alteration of an actor's performance through the enrichment of the captured data with semantics and animation properties. Hybrid geometry- and video-based animation methods are applied that allow a direct animation of the high-quality data itself instead of creating a CG model that resembles the captured data. Semantic enrichment and animation are achieved by establishing temporal consistency followed by automatic rigging of each 3D frame using a parametric human body model. The hybrid approach combines the flexibility of classical CG animation with the realism of real captured data. For the face, coarse movements are modelled in the geometry only, while very fine and subtle details, often lacking in purely geometric methods, are captured in video textures, which can interactively be combined to form new facial expressions. On top of that, regions that are challenging to synthesise, such as the teeth or the eyes, are learned and filled in realistically in an autoencoder-based approach. This study covers the full pipeline from capturing, volumetric video production, and enrichment with semantics for the final hybrid animation. |
Starting Page | 350 |
Ending Page | 358 |
Page Count | 9 |
ISSN | 17519632 |
Volume Number | 14 |
e-ISSN | 17519640 |
Issue Number | Issue 6, Sep (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cvi/14/6 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0786 |
Journal | IET Computer Vision |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2020-04-15 |
Access Restriction | Open |
Rights License | Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) |
Subject Keyword | 3D Data Actor Performance Animation Property Autoencoder-based Approach Automatic Rigging Classical CG Animation Computational Geometry Computer Animation Computer Vision And Image Processing Technique Deformation Property Facial Expressions Free-viewpoint Volumetric Video Geometric Animation Ability Graphics Technique High-quality Animatable Volumetric Video Content High-quality Data Human Performances Hybrid Animation Hybrid Geometry Image Texture Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Parametric Shape Adaptive Full Human Body Model Pose Editing Semantic Enrichment Temporal Consistency Video Signal Processing Video-based Animation Approach Video-based Textures |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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