Transformer encoder architectures have recently achieved state-of-the-art
results on monocular 3D human mesh reconstruction, but they require a
substantial number of parameters and expensive computations. Due to the large
memory overhead and slow inference speed, it is difficult to deploy such models
for practical use. In this paper, we propose a novel transformer
encoder-decoder architecture for 3D human mesh reconstruction from a single
image, called FastMETRO. We identify the performance bottleneck in the
encoder-based transformers is caused by the token design which introduces high
complexity interactions among input tokens. We disentangle the interactions via
an encoder-decoder architecture, which allows our model to demand much fewer
parameters and shorter inference time. In addition, we impose the prior
knowledge of human body's morphological relationship via attention masking and
mesh upsampling operations, which leads to faster convergence with higher
accuracy. Our FastMETRO improves the Pareto-front of accuracy and efficiency,
and clearly outperforms image-based methods on Human3.6M and 3DPW. Furthermore,
we validate its generalizability on FreiHAND.

本文研究了 Transformer Encoder 在 3D 人类网格重建上的应用。提出了一种名为 FastMETRO 的新型 Transformer Encoder-Decoder 结构，对输入标记的复杂性进行了解耦，并通过注意掩码和网格上采样操作引入人体形态学关系的先验知识来提高其收敛速度和准确性。在 Human3.6M 和 3DPW 数据集上，和基于图像的方法相比明显更有效。