VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent
  Space
Guénolé Fiche Simon Leglaive Xavier Alameda-Pineda Antonio Agudo Francesc Moreno-Noguer
Human Pose and Shape Estimation (HPSE) from RGB images can be broadly
categorized into two main groups: parametric and non-parametric approaches.
Parametric techniques leverage a low-dimensional statistical body model for
realistic results, whereas recent non-parametric methods achieve higher
precision by directly regressing the 3D coordinates of the human body. Despite
their strengths, both approaches face limitations: the parameters of
statistical body models pose challenges as regression targets, and predicting
3D coordinates introduces computational complexities and issues related to
smoothness. In this work, we take a novel approach to address the HPSE problem.
We introduce a unique method involving a low-dimensional discrete latent
representation of the human mesh, framing HPSE as a classification task.
Instead of predicting body model parameters or 3D vertex coordinates, our focus
is on forecasting the proposed discrete latent representation, which can be
decoded into a registered human mesh. This innovative paradigm offers two key
advantages: firstly, predicting a low-dimensional discrete representation
confines our predictions to the space of anthropomorphic poses and shapes;
secondly, by framing the problem as a classification task, we can harness the
discriminative power inherent in neural networks. Our proposed model, VQ-HPS, a
transformer-based architecture, forecasts the discrete latent representation of
the mesh, trained through minimizing a cross-entropy loss. Our results
demonstrate that VQ-HPS outperforms the current state-of-the-art non-parametric
approaches while yielding results as realistic as those produced by parametric
methods. This highlights the significant potential of the classification
approach for HPSE.