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With the recent surge in popularity of AR/VR applications, realistic andaccurate control of 3D full-body avatars has become a highly demanded feature.A particular challenge is that only a sparse tracking signal is available fromstandalone HMDs (Head Mounted Devices), often limited to tracking the user'shead and wrists. While this signal is resourceful for reconstructing the upperbody motion, the lower body is not tracked and must be synthesized from thelimited information provided by the upper body joints. In this paper, wepresent AGRoL, a novel conditional diffusion model specifically designed totrack full bodies given sparse upper-body tracking signals. Our model is basedon a simple multi-layer perceptron (MLP) architecture and a novel conditioningscheme for motion data. It can predict accurate and smooth full-body motion,particularly the challenging lower body movement. Unlike common diffusionarchitectures, our compact architecture can run in real-time, making itsuitable for online body-tracking applications. We train and evaluate our modelon AMASS motion capture dataset, and demonstrate that our approach outperformsstate-of-the-art methods in generated motion accuracy and smoothness. Wefurther justify our design choices through extensive experiments and ablationstudies.

Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model