Computer Animation and Virtual Worlds

Cover image for Vol. 23 Issue 6

November/December 2012

Volume 23, Issue 6

Pages i–ii, 533–578

  1. Issue Information

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    5. Research Article
    1. ISSUE INFORMATION (pages i–ii)

      Article first published online: 6 DEC 2012 | DOI: 10.1002/cav.1494

  2. Editorial

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    5. Research Article
    1. Editorial Issue 23.6 (page 533)

      Nadia Magnenat-Thalmann and Daniel Thalmann

      Article first published online: 6 DEC 2012 | DOI: 10.1002/cav.1493

  3. Special Issue Papers

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    5. Research Article
    1. A navigation mesh for dynamic environments (pages 535–546)

      Wouter G. van Toll, Atlas F. Cook IV and Roland Geraerts

      Article first published online: 12 JUN 2012 | DOI: 10.1002/cav.1468

      Thumbnail image of graphical abstract

      Games and simulations frequently model scenarios where obstacles move, appear, and disappear in an environment. We show how to maintain a 2D or 2.5D navigation mesh in an environment that contains such dynamic polygonal obstacles. Experiments show that local updates are fast enough to permit real-time updates of the navigation mesh.

    2. Planning interactive task for intelligent characters (pages 547–557)

      Dan Zong, Chunpeng Li, Shihong Xia and Zhaoqi Wang

      Article first published online: 15 JUN 2012 | DOI: 10.1002/cav.1470

      Thumbnail image of graphical abstract

      This paper presents a novel method to plan interactive task based on Q-learning for intelligent characters. Firstly, in the data preprocessing phase, we abstract the motion clips as high-level behaviors and construct the interactive behavior graph to define the interactive capabilities in terms of interactive features. Secondly, for the controller training phase, Q-learning algorithm is employed to train the controller. Finally, in the motion-synthesis phase, the optimal motion sequences can be generated by following the controller to accomplish the interactive task. The experiments demonstrate that our framework can generate realistic motion sequences to plan interactive task in complex environment.

    3. Optimized keyframe extraction for 3D character animations (pages 559–568)

      Chao Jin, Thomas Fevens and Sudhir Mudur

      Article first published online: 2 JUL 2012 | DOI: 10.1002/cav.1471

      Thumbnail image of graphical abstract

      Using an animation saliency map computed in the high-dimensional space of the character's geometric model and a lowdimensional embedding of the animation sequence obtained using LLE, we extract an optimized keyframe representation of the animation that can reconstruct the original animation with less-perceived error. The keyframe search is carried out in the low-dimensional embedding space, making it efficient. This approach yields much better results than earlier methods for keyframe extraction.

  4. Research Article

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    5. Research Article
    1. Politeness improves interactivity in dense crowds (pages 569–578)

      Brian F. Allen, Nadia Magnenat-Thalmann and Daniel Thalmann

      Article first published online: 29 JUN 2012 | DOI: 10.1002/cav.1472

      Thumbnail image of graphical abstract

      Controlling a virtual avatar within a densely populated environment can be challenging because the surrounding virtual characters may obstruct the user's path. Predicting the user's intentions and steering surrounding agents to avoid the user's future path improve the user's navigability in a crowded virtual environment. Observers report that the “polite” virtual characters are more natural and human-like.

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