Real-time feature-based image morphing for memory-efficient impostor rendering and animation on GPU


Real-time rendering of large animated crowds consisting of thousands of virtual humans is important for several applications including simulations, games, and interactive walkthroughs but cannot be performed using complex polygonal models at interactive frame rates. For that reason, methods using large numbers of precomputed image-based representations, called impostors, have been proposed. These methods take advantage of existing programmable graphics hardware to compensate for computational expense while maintaining visual fidelity. Thanks to these methods, the number of different virtual humans rendered in real time is no longer restricted by computational power but by texture memory consumed for the variety and discretization of their animations. This work proposes a resource-efficient impostor rendering methodology that employs image morphing techniques to reduce memory consumption while preserving perceptual quality, thus allowing higher diversity or resolution of the rendered crowds. Results of the experiments indicated that the proposed method, in comparison with conventional impostor rendering techniques, can obtain 38 % smoother animations or 87 % better appearance quality by reducing the number of key-frames required for preserving the animation quality via resynthesizing them with up to 92 % similarity on real time.

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The authors would like to thank anonymous reviewers at CW2011 and TVCJ for their comments that helped us to improve the quality of this manuscript.

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Correspondence to Kamer Ali Yuksel.

Additional information

This work has been partially supported by TUBITAK under project 109E134, VIPSAFE.

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Yuksel, K.A., Yucebilgin, A., Balcisoy, S. et al. Real-time feature-based image morphing for memory-efficient impostor rendering and animation on GPU. Vis Comput 29, 131–140 (2013).

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  • Crowd simulation
  • Impostor rendering
  • Image and view morphing
  • Graphical processing unit