  Training state-of-the-art models for human body pose and shape recovery from
images or videos requires datasets with corresponding annotations that are
really hard and expensive to obtain. Our goal in this paper is to study whether
poses from 3D Motion Capture (MoCap) data can be used to improve image-based
and video-based human mesh recovery methods. We find that fine-tune image-based
models with synthetic renderings from MoCap data can increase their
performance, by providing them with a wider variety of poses, textures and
backgrounds. In fact, we show that simply fine-tuning the batch normalization
layers of the model is enough to achieve large gains. We further study the use
of MoCap data for video, and introduce PoseBERT, a transformer module that
directly regresses the pose parameters and is trained via masked modeling. It
is simple, generic and can be plugged on top of any state-of-the-art
image-based model in order to transform it in a video-based model leveraging
temporal information. Our experimental results show that the proposed
approaches reach state-of-the-art performance on various datasets including
3DPW, MPI-INF-3DHP, MuPoTS-3D, MCB and AIST. Test code and models will be
available soon.
