Fast 3D body reconstruction of continuous 2D human joint data

Yu An; GuoWei Wang; LiPeng Li; Ning Li; YiFei Li

doi:10.1117/12.2637356

15 June 2022 Fast 3D body reconstruction of continuous 2D human joint data

Yu An, GuoWei Wang, LiPeng Li, Ning Li, YiFei Li

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122851E (2022) https://doi.org/10.1117/12.2637356
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

The development of methods and tools for 3D body reconstruction has become an important research area in computer animation. Relevant research shows that 3D reconstruction directly depends on monocular video data needs a lot of computation. Therefore, existing methods can’t meet the requirements of fast real-time 3D body joint computing. With the help of deep learning and pattern recognition, we can easily obtain a large number of continuous two-dimensional joint data from video. In this paper, we propose a fast 3D body reconstruction method with continuous two-dimensional human joint data. We use the expected position to solve the problem of Ambiguity from monocular data restoration. Base on the predefined body pose, we can get continuous 3D joint data quickly, and our result show this method has a good performance in the situation of rapid change of motion amplitude and speed.

Citation Download Citation

Yu An, GuoWei Wang, LiPeng Li, Ning Li, and YiFei Li "Fast 3D body reconstruction of continuous 2D human joint data", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122851E (15 June 2022); https://doi.org/10.1117/12.2637356

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