Existing methods for human mesh recovery mainly focus on single-view
frameworks, but they often fail to produce accurate results due to the
ill-posed setup. Considering the maturity of the multi-view motion capture
system, in this paper, we propose to solve the prior ill-posed problem by
leveraging multiple images from different views, thus significantly enhancing
the quality of recovered meshes. In particular, we present a novel
\textbf{M}ulti-view human body \textbf{M}esh \textbf{T}ranslator (MMT) model
for estimating human body mesh with the help of vision transformer.
Specifically, MMT takes multi-view images as input and translates them to
targeted meshes in a single-forward manner. MMT fuses features of different
views in both encoding and decoding phases, leading to representations embedded
with global information. Additionally, to ensure the tokens are intensively
focused on the human pose and shape, MMT conducts cross-view alignment at the
feature level by projecting 3D keypoint positions to each view and enforcing
their consistency in geometry constraints. Comprehensive experiments
demonstrate that MMT outperforms existing single or multi-view models by a
large margin for human mesh recovery task, notably, 28.8\% improvement in MPVE
over the current state-of-the-art method on the challenging HUMBI dataset.
Qualitative evaluation also verifies the effectiveness of MMT in reconstructing
high-quality human mesh. Codes will be made available upon acceptance.

本文提出一种新的多视角人体网格翻译器（MMT）模型，通过利用多视角图像和视觉转换器来解决单视角方法的病态问题，同时在编码和解码阶段融合不同视角的特征，并通过交叉视图对齐来确保令牌集中于人类姿势和形状。实验表明，MMT 模型在人体网格恢复任务上表现出很大优势，特别是在具有挑战性的 HUMBI 数据集上的 MPVE 方面，优于现有的单视角或多视角模型，提高 28.8％。