  Traditional methods of reconstructing 3D human pose and mesh from single
images rely on paired image-mesh datasets, which can be difficult and expensive
to obtain. Due to this limitation, model scalability is constrained as well as
reconstruction performance. Towards addressing the challenge, we introduce Mesh
Pre-Training (MPT), an effective pre-training strategy that leverages large
amounts of MoCap data to effectively perform pre-training at scale. We
introduce the use of MoCap-generated heatmaps as input representations to the
mesh regression transformer and propose a Masked Heatmap Modeling approach for
improving pre-training performance. This study demonstrates that pre-training
using the proposed MPT allows our models to perform effective inference without
requiring fine-tuning. We further show that fine-tuning the pre-trained MPT
model considerably improves the accuracy of human mesh reconstruction from
single images. Experimental results show that MPT outperforms previous
state-of-the-art methods on Human3.6M and 3DPW datasets. As a further
application, we benchmark and study MPT on the task of 3D hand reconstruction,
showing that our generic pre-training scheme generalizes well to hand pose
estimation and achieves promising reconstruction performance.
