Reconstructing Humans with a Biomechanically Accurate Skeleton
Authors: name: Yan Xia, name: Xiaowei Zhou, name: Etienne Vouga, name: Qixing Huang, name: Georgios Pavlakos

In this paper, we introduce a method for reconstructing 3D humans from a
single image using a biomechanically accurate skeleton model. To achieve this,
we train a transformer that takes an image as input and estimates the
parameters of the model. Due to the lack of training data for this task, we
build a pipeline to produce pseudo ground truth model parameters for single
images and implement a training procedure that iteratively refines these pseudo
labels. Compared to state-of-the-art methods for 3D human mesh recovery, our
model achieves competitive performance on standard benchmarks, while it
significantly outperforms them in settings with extreme 3D poses and
viewpoints. Additionally, we show that previous reconstruction methods
frequently violate joint angle limits, leading to unnatural rotations. In
contrast, our approach leverages the biomechanically plausible degrees of
freedom making more realistic joint rotation estimates. We validate our
approach across multiple human pose estimation benchmarks. We make the code,
models and data available at: https://isshikihugh.github.io/HSMR/