School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China.
Laboratory for Artificial Intelligence in Design, Hong Kong, China.
PLoS One. 2024 Feb 26;19(2):e0299040. doi: 10.1371/journal.pone.0299040. eCollection 2024.
Understanding the dynamic deformation pattern and biomechanical properties of breasts is crucial in various fields, including designing ergonomic bras and customized prostheses, as well as in clinical practice. Previous studies have recorded and analyzed the dynamic behaviors of the breast surface using 4D scanning, which provides a sequence of 3D meshes during movement with high spatial and temporal resolutions. However, these studies are limited by the lack of robust and automated data processing methods which result in limited data coverage or error-prone analysis results. To address this issue, we identify revealing inter-frame dense correspondence as the core challenge towards conducting reliable and consistent analysis of the 4D scanning data. We proposed a fully-automatic approach named Ulta-dense Motion Capture (UdMC) using Thin-plate Spline (TPS) to augment the sparse landmarks recorded via motion capture (MoCap) as initial dense correspondence and then rectified it with a sophisticated post-alignment scheme. Two downstream tasks are demonstrated to validate its applicability: virtual landmark tracking and deformation intensity analysis. For evaluation, a dynamic 4D human breast anthropometric dataset DynaBreastLite was constructed. The results show that our approach can robustly capture the dynamic deformation characteristics of the breast surfaces, significantly outperforms baselines adapted from previous works in terms of accuracy, consistency, and efficiency. For 10 fps dataset, average error of 0.25 cm on control-landmarks and 0.33 cm on non-control (arbitrary) landmarks were achieved, with 17-70 times faster computation time. Evaluation was also carried out on 60 fps and 120 fps datasets, with consistent and large performance gaining being observed. The proposed method may contribute to advancing research in breast anthropometry, biomechanics, and ergonomics by enabling more accurate tracking of the breast surface deformation patterns and dynamic characteristics.
理解乳房的动态变形模式和生物力学特性在多个领域至关重要,包括设计符合人体工程学的胸罩和定制义乳,以及临床实践。以前的研究已经使用 4D 扫描记录和分析了乳房表面的动态行为,该方法提供了在运动过程中具有高空间和时间分辨率的 3D 网格序列。然而,这些研究受到缺乏稳健和自动化数据处理方法的限制,导致数据覆盖范围有限或分析结果容易出错。为了解决这个问题,我们确定揭示帧间密集对应关系是进行 4D 扫描数据可靠和一致分析的核心挑战。我们提出了一种名为 Ulta-dense Motion Capture(UdMC)的全自动方法,该方法使用薄板样条(TPS)来增强通过运动捕捉(MoCap)记录的稀疏标记作为初始密集对应关系,然后使用复杂的后配准方案对其进行校正。展示了两个下游任务来验证其适用性:虚拟标记跟踪和变形强度分析。为了评估,构建了一个动态 4D 人体乳房人体测量数据集 DynaBreastLite。结果表明,我们的方法可以稳健地捕捉乳房表面的动态变形特征,在准确性、一致性和效率方面明显优于以前工作中的基线。对于 10 fps 数据集,在控制标记上的平均误差为 0.25cm,在非控制(任意)标记上的平均误差为 0.33cm,计算时间快 17-70 倍。还在 60 fps 和 120 fps 数据集上进行了评估,观察到一致且显著的性能提升。该方法可以通过更准确地跟踪乳房表面的变形模式和动态特征,为乳房人体测量学、生物力学和人体工程学的研究做出贡献。