  This paper addresses the problem of monocular 3D human shape and pose
estimation from an RGB image. Despite great progress in this field in terms of
pose prediction accuracy, state-of-the-art methods often predict inaccurate
body shapes. We suggest that this is primarily due to the scarcity of
in-the-wild training data with diverse and accurate body shape labels. Thus, we
propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system
that utilises proxy representations, such as silhouettes and 2D joints, as
inputs to a shape and pose regression neural network, which is trained with
synthetic training data (generated on-the-fly during training using the SMPL
statistical body model) to overcome data scarcity. We bridge the gap between
synthetic training inputs and noisy real inputs, which are predicted by
keypoint detection and segmentation CNNs at test-time, by using data
augmentation and corruption during training. In order to evaluate our approach,
we curate and provide a challenging evaluation dataset for monocular human
shape estimation, Sports Shape and Pose 3D (SSP-3D). It consists of RGB images
of tightly-clothed sports-persons with a variety of body shapes and
corresponding pseudo-ground-truth SMPL shape and pose parameters, obtained via
multi-frame optimisation. We show that STRAPS outperforms other
state-of-the-art methods on SSP-3D in terms of shape prediction accuracy, while
remaining competitive with the state-of-the-art on pose-centric datasets and
metrics.
