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Traditional methods of reconstructing 3D human pose and mesh from singleimages rely on paired image-mesh datasets, which can be difficult and expensiveto obtain. Due to this limitation, model scalability is constrained as well asreconstruction performance. Towards addressing the challenge, we introduce MeshPre-Training (MPT), an effective pre-training strategy that leverages largeamounts of MoCap data to effectively perform pre-training at scale. Weintroduce the use of MoCap-generated heatmaps as input representations to themesh regression transformer and propose a Masked Heatmap Modeling approach forimproving pre-training performance. This study demonstrates that pre-trainingusing the proposed MPT allows our models to perform effective inference withoutrequiring fine-tuning. We further show that fine-tuning the pre-trained MPTmodel considerably improves the accuracy of human mesh reconstruction fromsingle images. Experimental results show that MPT outperforms previousstate-of-the-art methods on Human3.6M and 3DPW datasets. As a furtherapplication, we benchmark and study MPT on the task of 3D hand reconstruction,showing that our generic pre-training scheme generalizes well to hand poseestimation and achieves promising reconstruction performance.

MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction