MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction
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We present Mesh Pre-Training (MPT), a new pre-training framework that leverages 3D mesh data such as MoCap data for human pose and mesh reconstruction from a single image. Existing work in 3D pose and mesh reconstruction typically requires image-mesh pairs as the training data, but the acquisition of 2D-to-3D annotations is difficult. In this paper, we explore how to leverage 3D mesh data such as MoCap data, that does not have RGB images, for pre-training. The key idea is that even though 3D mesh data cannot be used for end-to-end training due to a lack of the corresponding RGB images, it can be used to pre-train the mesh regression transformer subnetwork. We observe that such pre-training not only improves the accuracy of mesh reconstruction from a single image, but also enables zero-shot capability. We conduct mesh pre-training using 2 million meshes. Experimental results show that MPT advances the state-of-the-art results on Human3.6M and 3DPW datasets. We also show that MPT enables transformer models to have zero-shot capability of human mesh reconstruction from real images. In addition, we demonstrate the generalizability of MPT to 3D hand reconstruction, achieving state-of-the-art results on FreiHAND dataset.
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