Three-dimensional animation compression optimization based on structured processing and adaptive spatiotemporal segmentation algorithm

Xuan Wang, Xinran Yan, Biao Liu, Kecen Liu and Yan Su

PLOS ONE, 2026, vol. 21, issue 9, 1-25

Abstract: With the rapid development of virtual reality, game engines, and digital twin technologies, the massive size and significant inter-frame temporal redundancy of three-dimensional (3D) animation data have limited its application in cross-platform transmission and real-time rendering. To design a compression algorithm with high compression efficiency, low reconstruction error, and strong adaptability, this paper proposes a 3D animation compression optimization scheme that integrates structured processing and adaptive spatio-temporal segmentation. First, the unordered vertex data is normalized using an improved equal-cluster K-means (eK-means) algorithm. Then, a 3D animation compression algorithm based on adaptive spatio-temporal segmentation (3DACTAS) is constructed to dynamically generate spatio-temporal segmentation blocks. Finally, an optimization algorithm, I3DACTAS, is proposed by combining boundary editing (BE) and matrix reorganization (MR). Experiments show that the I3DACTAS algorithm achieves an average compression ratio of 6.97 on four datasets, a 28.3% improvement over the comparative algorithm AG-3DAC; the average reconstruction error is reduced by 38.7%, and the average structural similarity index reaches 0.934; mobile animation loading time is reduced by more than 72.2%, and the average rendering frame rate is increased by 44.7%. This research solves problems such as inconsistent segmentation boundaries and incomplete redundancy removal in traditional algorithms, providing efficient technical support for real-time interactive 3D animation across platforms and promoting its widespread application on resource-constrained devices.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0357382

DOI: 10.1371/journal.pone.0357382

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