Human mesh recovery from arbitrary multi-view images involves two
characteristics: the arbitrary camera poses and arbitrary number of camera
views. Because of the variability, designing a unified framework to tackle this
task is challenging. The challenges can be summarized as the dilemma of being
able to simultaneously estimate arbitrary camera poses and recover human mesh
from arbitrary multi-view images while maintaining flexibility. To solve this
dilemma, we propose a divide and conquer framework for Unified Human Mesh
Recovery (U-HMR) from arbitrary multi-view images. In particular, U-HMR
consists of a decoupled structure and two main components: camera and body
decoupling (CBD), camera pose estimation (CPE), and arbitrary view fusion
(AVF). As camera poses and human body mesh are independent of each other, CBD
splits the estimation of them into two sub-tasks for two individual
sub-networks (\ie, CPE and AVF) to handle respectively, thus the two sub-tasks
are disentangled. In CPE, since each camera pose is unrelated to the others, we
adopt a shared MLP to process all views in a parallel way. In AVF, in order to
fuse multi-view information and make the fusion operation independent of the
number of views, we introduce a transformer decoder with a SMPL parameters
query token to extract cross-view features for mesh recovery. To demonstrate
the efficacy and flexibility of the proposed framework and effect of each
component, we conduct extensive experiments on three public datasets:
Human3.6M, MPI-INF-3DHP, and TotalCapture.

从任意多视角图像中恢复人体网格涉及两个特点：任意的相机姿态和任意数量的相机视图。为了解决这个问题，我们提出了一个分治框架，用于从任意多视角图像中统一恢复人体网格。