  Remarkable strides have been made in reconstructing static scenes or human
bodies from monocular videos. Yet, the two problems have largely been
approached independently, without much synergy. Most visual SLAM methods can
only reconstruct camera trajectories and scene structures up to scale, while
most HMR methods reconstruct human meshes in metric scale but fall short in
reasoning with cameras and scenes. This work introduces Synergistic Camera and
Human Reconstruction (SynCHMR) to marry the best of both worlds. Specifically,
we design Human-aware Metric SLAM to reconstruct metric-scale camera poses and
scene point clouds using camera-frame HMR as a strong prior, addressing depth,
scale, and dynamic ambiguities. Conditioning on the dense scene recovered, we
further learn a Scene-aware SMPL Denoiser to enhance world-frame HMR by
incorporating spatio-temporal coherency and dynamic scene constraints.
Together, they lead to consistent reconstructions of camera trajectories, human
meshes, and dense scene point clouds in a common world frame. Project page:
https://paulchhuang.github.io/synchmr
