TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer

2023 IEEE/CVF International Conference on Computer Vision (ICCV)(2022)

Cited 19|Views66
No score
Abstract
In this paper, we introduce a set of 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. As a result, our method vastly reduces the number of tokens involved in high-complexity interactions in the Transformer, achieving competitive accuracy of shape recovery at a significantly reduced computational cost. We conduct extensive experiments across a wide range of benchmarks to validate the proposed method and further demonstrate the generalizability of our method on hand mesh recovery. Our code will be publicly available once the paper is published.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined