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Cage-based Performance Capture

  • Book
  • © 2014

Overview

Authors:
  1. Yann Savoye

    1. Lyon, France

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  • Recent research on Cage-Based Performance Capture
  • Presents a graphic-to-vision strategy for performance capture
  • Written by an expert in the field
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 509)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-x
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  2. General Introduction

    • Yann Savoye
    Pages 1-15

  3. Sparse Constraints Over Animatable Subspaces

    • Yann Savoye
    Pages 17-51

  4. Reusing Performance Capture Data

    • Yann Savoye
    Pages 53-92

  5. Toward Non-rigid Dynamic Cage Capture

    • Yann Savoye
    Pages 93-134

  6. Conclusions

    • Yann Savoye
    Pages 135-141
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Keywords

About this book

Nowadays, highly-detailed animations of live-actor performances are increasingly easier to acquire and 3D Video has reached considerable attentions in visual media production. In this book, we address the problem of extracting or acquiring and then reusing non-rigid parametrization for video-based animations. 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. To solve this challenge, we directly rely on a skin-detached dimension reduction thanks to the well-known cage-based paradigm. First, we achieve Scalable Inverse Cage-based Modeling by transposing the inverse kinematics paradigm on surfaces. Thus, we introduce a cage inversion process with user-specified screen-space constraints. Secondly, we convert non-rigid animated surfaces into a sequence of optimal cage parameters via Cage-based Animation Conversion. Building upon this reskinning procedure, we also develop a well-formed Animation Cartoonization algorithm for multi-view data in term of cage-based surface exaggeration and video-based appearance stylization. Thirdly, motivated by the relaxation of prior knowledge on the data, we propose a promising unsupervised approach to perform Iterative Cage-based Geometric Registration. This novel registration scheme deals with reconstructed target point clouds obtained from multi-view video recording, in conjunction with a static and wrinkled template mesh. Above all, we demonstrate the strength of cage-based subspaces in order to reparametrize highly non-rigid dynamic surfaces, without the need of secondary deformations. To the best of our knowledge this book opens the field of Cage-based Performance Capture.

Authors and Affiliations

  • Lyon, France

    Yann Savoye

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