  Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous
problem, as similar 2D projections can correspond to multiple 3D
interpretations. Nevertheless, most HMR methods overlook this ambiguity and
make a single prediction without accounting for the associated uncertainty. A
few approaches generate a distribution of human meshes, enabling the sampling
of multiple predictions; however, none of them is competitive with the latest
single-output model when making a single prediction. This work proposes a new
approach based on masked generative modeling. By tokenizing the human pose and
shape, we formulate the HMR task as generating a sequence of discrete tokens
conditioned on an input image. We introduce MEGA, a MaskEd Generative
Autoencoder trained to recover human meshes from images and partial human mesh
token sequences. Given an image, our flexible generation scheme allows us to
predict a single human mesh in deterministic mode or to generate multiple human
meshes in stochastic mode. MEGA enables us to propose multiple outputs and to
evaluate the uncertainty of the predictions. Experiments on in-the-wild
benchmarks show that MEGA achieves state-of-the-art performance in
deterministic and stochastic modes, outperforming single-output and
multi-output approaches.
