Parametric 3D human models such as SMPL have driven significant advances in human pose and shape estimation, yet their simplified kinematics limit biomechanical realism. The recently proposed SKEL model addresses this limitation by re-rigging SMPL with an anatomically accurate skeleton. However, estimating SKEL parameters directly remains challenging due to limited training data, perspective ambiguities, and the inherent complexity of human articulation. We introduce SKEL-CF, a coarse-to-fine framework for SKEL parameter estimation. SKEL-CF employs a transformer-based encoder-decoder architecture, where the encoder predicts coarse camera and SKEL parameters, and the decoder progressively refines them in successive layers. To ensure anatomically consistent supervision, we convert the existing SMPL-based dataset 4DHuman into a SKEL-aligned version, 4DHuman-SKEL, providing high-quality training data for SKEL estimation. In addition, to mitigate depth and scale ambiguities, we explicitly incorporate camera modeling into the SKEL-CF pipeline and demonstrate its importance across diverse viewpoints. Extensive experiments validate the effectiveness of the proposed design. On the challenging MOYO dataset, SKEL-CF achieves 85.0 MPJPE / 51.4 PA-MPJPE, significantly outperforming the previous SKEL-based state-of-the-art HSMR (104.5 / 79.6). These results establish SKEL-CF as a scalable and anatomically faithful framework for human motion analysis, facilitating the use of computer vision techniques in biomechanics-related analysis. Our implementation is available on the project page: https://pokerman8.github.io/SKEL-CF/.

翻译：SMPL等参数化三维人体模型虽推动了人体姿态与形状估计领域的显著进展，但其简化的运动学限制了生物力学的真实感。近期提出的SKEL模型通过为SMPL装配解剖学精确的骨架，解决了这一局限。然而，由于训练数据有限、透视歧义以及人体关节固有的复杂性，直接估计SKEL参数仍具挑战性。本文提出SKEL-CF，一种用于SKEL参数估计的从粗到精框架。SKEL-CF采用基于Transformer的编码器-解码器架构：编码器预测粗略的相机与SKEL参数，解码器在后续层中逐步优化这些参数。为确保解剖学一致的监督，我们将现有的基于SMPL的数据集4DHuman转换为SKEL对齐版本4DHuman-SKEL，为SKEL估计提供高质量训练数据。此外，为缓解深度与尺度歧义，我们显式地将相机建模整合到SKEL-CF流程中，并论证了其在多视角下的重要性。大量实验验证了所提设计的有效性。在具有挑战性的MOYO数据集上，SKEL-CF取得了85.0 MPJPE / 51.4 PA-MPJPE的指标，显著优于此前基于SKEL的先进方法HSMR（104.5 / 79.6）。这些结果表明SKEL-CF是一个可扩展且解剖学忠实的人体运动分析框架，有助于计算机视觉技术在生物力学相关分析中的应用。我们的实现已在项目页面发布：https://pokerman8.github.io/SKEL-CF/。