SKEL-CF: Coarse-to-Fine Biomechanical Skeleton and Surface Mesh Recovery

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. In this work, we propose SKEL-CF, a new framework for estimating SKEL parameters. SKEL-CF adopts a standard transformer-based encoder–decoder architecture. The encoder first produces coarse predictions of the camera extrinsics and SKEL parameters. The decoder then iteratively refines these predictions across multiple layers, with explicit and auxiliary supervision applied at each layer. To provide anatomically consistent training data, we convert the existing SMPL-based dataset into a SKEL-aligned version, called HMR-SKEL. This new dataset offers high-quality supervision for SKEL estimation. In addition, to reduce depth and scale ambiguity, we explicitly incorporate camera intrinsic estimation into the SKEL-CF pipeline and show that it is important for accurate reconstruction. 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 promising framework for human motion analysis, facilitating the use of computer vision techniques in biomechanicsrelated analysis. Our implementation is available on the project page: https://pokerman8.github.io/SKEL-CF/.