  Marker-less monocular 3D human motion capture (MoCap) with scene interactions
is a challenging research topic relevant for extended reality, robotics and
virtual avatar generation. Due to the inherent depth ambiguity of monocular
settings, 3D motions captured with existing methods often contain severe
artefacts such as incorrect body-scene inter-penetrations, jitter and body
floating. To tackle these issues, we propose HULC, a new approach for 3D human
MoCap which is aware of the scene geometry. HULC estimates 3D poses and dense
body-environment surface contacts for improved 3D localisations, as well as the
absolute scale of the subject. Furthermore, we introduce a 3D pose trajectory
optimisation based on a novel pose manifold sampling that resolves erroneous
body-environment inter-penetrations. Although the proposed method requires less
structured inputs compared to existing scene-aware monocular MoCap algorithms,
it produces more physically-plausible poses: HULC significantly and
consistently outperforms the existing approaches in various experiments and on
different metrics. Project page: https://vcai.mpi-inf.mpg.de/projects/HULC/.
