Fusion of motion smoothing algorithm and motion segmentation algorithm for human animation generation - Shinan Ding (PLoS ONE, 2025)
In the field of human animation generation, the existing technology is often limited by the dependence on large-scale data sets, and it is difficult to capture subtle dynamic changes when processing motion transitions, resulting in insufficient animation fluency and realism. In order to improve the naturalness and diversity of human animation generation, a method combining motion smoothing algorithm and motion segmentation algorithm is proposed. Firstly, the tree-level model based on human skeleton topology and bidirectional unbiased Kalman filter are used for noise reduction pre-processing of motion data to improve the accuracy of motion capture. Then, combining the discriminant analysis algorithm based on sparse reconstruction and the multi-scale temporal association segmentation algorithm, the key motion segments of the behavior pattern change are identified adaptively. The experimental results show that the accuracy of the proposed algorithm reaches 0.96 in coarse-grained segmentation and 0.91 in fine-grained segmentation, and the segmentation time is 15 seconds on average, which significantly exceeds the prior art. In addition, the algorithm shows superior results in color fidelity, detail representation, motion fluency, frame-to-frame coherence, overall animation consistency, action authenticity, and character expressiveness, and the average user satisfaction is above 0.85. The research not only enhances the naturalness and diversity of human body animation, but also provides a new impetus for technological advances in computer graphics, virtual reality and augmented reality.