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Keyframe extraction for motion capture data via pose saliency and reconstruction error

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Abstract

Keyframes are a summary representation of motion capture data, which provide the basis for compression, retrieval, overview and reuse of motion capture data. In this paper, a new approach is proposed to extract keyframes from motion capture data. This approach uses the angle of rotation of limbs and the distance between joints as the feature representation of human movement and calculates limb saliency based on the multiscale saliency of each motion feature. Then the weighted sum of limb saliency is defined as pose saliency, and the frames corresponding to the local maxima on the pose saliency curve are extracted as the initial keyframes. Finally, guided by the initial keyframes, the optimal keyframes are extracted based on the reconstruction error optimization algorithm. Experiments demonstrate that this approach can effectively extract the keyframes with high visual perceptual quality and low reconstruction error, and better meet the needs of real-time analysis and compression of motion capture data.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Nanchang University, Nanchang, 330031, Jiangxi, China

    Yungen Liu, Linfeng Chen & Zhenrong Lin

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  1. Yungen Liu
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  2. Linfeng Chen
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  3. Zhenrong Lin
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Correspondence to Zhenrong Lin.

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Liu, Y., Chen, L. & Lin, Z. Keyframe extraction for motion capture data via pose saliency and reconstruction error. Vis Comput 39, 4943–4953 (2023). https://doi.org/10.1007/s00371-022-02639-3

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