Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies, clothing
and viewpoint. Prior methods addressing this problem typically attempt to fit
parametric body models with certain priors on pose and shape. In this work we
argue for an alternative representation and propose BodyNet, a neural network
for direct inference of volumetric body shape from a single image. BodyNet is an
end-to-end trainable network that benefits from (i) a volumetric 3D loss, (ii) a
multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D
body part segmentation, and 3D pose. Each of them results in performance
improvement as demonstrated by our experiments. To evaluate the method, we fit
the SMPL model to our network output and show state-of-the-art results on the
SURREAL and Unite the People datasets, outperforming recent approaches. Besides
achieving state-of-the-art performance, our method also enables volumetric
body-part segmentation. More at: http://www.di.ens.fr/willow/research/bodynet/