In this paper, we introduce a set of simple yet effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performance is achieved by Transformer-based structures. However, they suffer from high model complexity and computation cost caused by redundant tokens. We propose token reduction strategies based on two important aspects, i.e., the 3D geometry structure and 2D image feature, where we hierarchically recover the mesh geometry with priors from body structure and conduct token clustering to pass fewer but more discriminative image feature tokens to the Transformer. Our method massively reduces the number of tokens involved in high-complexity interactions in the Transformer. This leads to a significantly reduced computational cost while still achieving competitive or even higher accuracy in shape recovery. Extensive experiments across a wide range of benchmarks validate the superior effectiveness of the proposed method. We further demonstrate the generalizability of our method on hand mesh recovery. Visit our project page at https://frank-zy-dou.github.io/projects/Tore/index.html.
@article{arxiv.2211.10705,
title = {TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer},
author = {Zhiyang Dou and Qingxuan Wu and Cheng Lin and Zeyu Cao and Qiangqiang Wu and Weilin Wan and Taku Komura and Wenping Wang},
journal= {arXiv preprint arXiv:2211.10705},
year = {2023}
}