  Estimating full-body human motion via sparse tracking signals from
head-mounted displays and hand controllers in 3D scenes is crucial to
applications in AR/VR. One of the biggest challenges to this task is the
one-to-many mapping from sparse observations to dense full-body motions, which
endowed inherent ambiguities. To help resolve this ambiguous problem, we
introduce a new framework to combine rich contextual information provided by
scenes to benefit full-body motion tracking from sparse observations. To
estimate plausible human motions given sparse tracking signals and 3D scenes,
we develop $\text{S}^2$Fusion, a unified framework fusing \underline{S}cene and
sparse \underline{S}ignals with a conditional dif\underline{Fusion} model.
$\text{S}^2$Fusion first extracts the spatial-temporal relations residing in
the sparse signals via a periodic autoencoder, and then produces time-alignment
feature embedding as additional inputs. Subsequently, by drawing initial noisy
motion from a pre-trained prior, $\text{S}^2$Fusion utilizes conditional
diffusion to fuse scene geometry and sparse tracking signals to generate
full-body scene-aware motions. The sampling procedure of $\text{S}^2$Fusion is
further guided by a specially designed scene-penetration loss and
phase-matching loss, which effectively regularizes the motion of the lower body
even in the absence of any tracking signals, making the generated motion much
more plausible and coherent. Extensive experimental results have demonstrated
that our $\text{S}^2$Fusion outperforms the state-of-the-art in terms of
estimation quality and smoothness.
