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

Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements