Abstract
This paper presents a very simple but efficient algorithm to normalize all motion data in database with same skeleton length. The input motion stream is processed sequentially while the computation for a single frame at each step requires only the results from the previous step over a neighborhood of nearby backward frames. In contrast to previous motion retargeting approaches, we simplify the constraint condition of retargeting problem, which leads to the simpler solutions. Moreover, we improve Shin et al.’s algorithm [10], which is adopted by a widely used Kovar’s footskate cleanup algorithm [6] through adding one case missed by it.
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Gao, Y., Ma, L., Chen, Z., Wu, X. (2005). Motion Normalization. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_13
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DOI: https://doi.org/10.1007/11573548_13
Publisher Name: Springer, Berlin, Heidelberg
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