  Dynamic multi-person mesh recovery has been a hot topic in 3D vision
recently. However, few works focus on the multi-person motion capture from
uncalibrated cameras, which mainly faces two challenges: the one is that
inter-person interactions and occlusions introduce inherent ambiguities for
both camera calibration and motion capture; The other is that a lack of dense
correspondences can be used to constrain sparse camera geometries in a dynamic
multi-person scene. Our key idea is incorporating motion prior knowledge into
simultaneous optimization of extrinsic camera parameters and human meshes from
noisy human semantics. First, we introduce a physics-geometry consistency to
reduce the low and high frequency noises of the detected human semantics. Then
a novel latent motion prior is proposed to simultaneously optimize extrinsic
camera parameters and coherent human motions from slightly noisy inputs.
Experimental results show that accurate camera parameters and human motions can
be obtained through one-stage optimization. The codes will be publicly
available at~\url{https://www.yangangwang.com}.

Computer Vision and Pattern Recognition