  Estimating 3D full-body avatars from AR/VR devices is essential for creating
immersive experiences in AR/VR applications. This task is challenging due to
the limited input from Head Mounted Devices, which capture only sparse
observations from the head and hands. Predicting the full-body avatars,
particularly the lower body, from these sparse observations presents
significant difficulties. In this paper, we are inspired by the inherent
property of the kinematic tree defined in the Skinned Multi-Person Linear
(SMPL) model, where the upper body and lower body share only one common
ancestor node, bringing the potential of decoupled reconstruction. We propose a
stratified approach to decouple the conventional full-body avatar
reconstruction pipeline into two stages, with the reconstruction of the upper
body first and a subsequent reconstruction of the lower body conditioned on the
previous stage. To implement this straightforward idea, we leverage the latent
diffusion model as a powerful probabilistic generator, and train it to follow
the latent distribution of decoupled motions explored by a VQ-VAE
encoder-decoder model. Extensive experiments on AMASS mocap dataset demonstrate
our state-of-the-art performance in the reconstruction of full-body motions.
