A Simple Baseline for Efficient Hand Mesh Reconstruction
Zhishan Zhou Shihao. zhou Zhi Lv Minqiang Zou Yao Tang Jiajun Liang
3D hand pose estimation has found broad application in areas such as gesture
recognition and human-machine interaction tasks. As performance improves, the
complexity of the systems also increases, which can limit the comparative
analysis and practical implementation of these methods. In this paper, we
propose a simple yet effective baseline that not only surpasses
state-of-the-art (SOTA) methods but also demonstrates computational efficiency.
To establish this baseline, we abstract existing work into two components: a
token generator and a mesh regressor, and then examine their core structures. A
core structure, in this context, is one that fulfills intrinsic functions,
brings about significant improvements, and achieves excellent performance
without unnecessary complexities. Our proposed approach is decoupled from any
modifications to the backbone, making it adaptable to any modern models. Our
method outperforms existing solutions, achieving state-of-the-art (SOTA)
results across multiple datasets. On the FreiHAND dataset, our approach
produced a PA-MPJPE of 5.7mm and a PA-MPVPE of 6.0mm. Similarly, on the Dexycb
dataset, we observed a PA-MPJPE of 5.5mm and a PA-MPVPE of 5.0mm. As for
performance speed, our method reached up to 33 frames per second (fps) when
using HRNet and up to 70 fps when employing FastViT-MA36