  Easy access to precise 3D tracking of movement could benefit many aspects of
rehabilitation. A challenge to achieving this goal is that while there are many
datasets and pretrained algorithms for able-bodied adults, algorithms trained
on these datasets often fail to generalize to clinical populations including
people with disabilities, infants, and neonates. Reliable movement analysis of
infants and neonates is important as spontaneous movement behavior is an
important indicator of neurological function and neurodevelopmental disability,
which can help guide early interventions. We explored the application of
dynamic Gaussian splatting to sparse markerless motion capture (MMC) data. Our
approach leverages semantic segmentation masks to focus on the infant,
significantly improving the initialization of the scene. Our results
demonstrate the potential of this method in rendering novel views of scenes and
tracking infant movements. This work paves the way for advanced movement
analysis tools that can be applied to diverse clinical populations, with a
particular emphasis on early detection in infants.
