We propose a method to address the challenge of generating a 3D digital twin of a patient during an X-ray guided medical procedure from a single 2D X-ray projection image, a problem that is inherently ill-posed. To tackle this issue, we aim to infer the parameters of Bones, Organs and Skin Shape (BOSS) model, a deformable human shape and pose model. There are currently two main approaches for model-based estimation. Optimization-based methods try to iteratively fit a body model to 2D measurements, they produce accurate 2D alignments but are slow and sensitive to initialization. On the other hand, regression-based methods use neural networks to estimate the model parameters directly, resulting in faster predictions but often with misalignments. Our approach combines the benefits of both techniques by implementing a fully differentiable paradigm through the use of higher-order optimizers that only require the Jacobian, which can be determined implicitly. The network was trained on synthetic CT and real CBCT image data, ensuring view independence. We demonstrate the potential clinical applicability of our method by validating it on multiple datasets covering diverse anatomical regions, and achieving an error of 27.98 mm.