  Mixed reality applications require tracking the user's full-body motion to
enable an immersive experience. However, typical head-mounted devices can only
track head and hand movements, leading to a limited reconstruction of full-body
motion due to variability in lower body configurations. We propose BoDiffusion
-- a generative diffusion model for motion synthesis to tackle this
under-constrained reconstruction problem. We present a time and space
conditioning scheme that allows BoDiffusion to leverage sparse tracking inputs
while generating smooth and realistic full-body motion sequences. To the best
of our knowledge, this is the first approach that uses the reverse diffusion
process to model full-body tracking as a conditional sequence generation task.
We conduct experiments on the large-scale motion-capture dataset AMASS and show
that our approach outperforms the state-of-the-art approaches by a significant
margin in terms of full-body motion realism and joint reconstruction error.
