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Motion capture (mocap) and time-of-flight based sensing of human actions arebecoming increasingly popular modalities to perform robust activity analysis.Applications range from action recognition to quantifying movement quality forhealth applications. While marker-less motion capture has made great progress,in critical applications such as healthcare, marker-based systems, especiallyactive markers, are still considered gold-standard. However, there are severalpractical challenges in both modalities such as visibility, tracking errors,and simply the need to keep marker setup convenient wherein movements arerecorded with a reduced marker-set. This implies that certain joint locationswill not even be marked-up, making downstream analysis of full body movementchallenging. To address this gap, we first pose the problem of reconstructingthe unmarked joint data as an ill-posed linear inverse problem. We recovermissing joints for a given action by projecting it onto the manifold of humanactions, this is achieved by optimizing the latent space representation of adeep autoencoder. Experiments on both mocap and Kinect datasets clearlydemonstrate that the proposed method performs very well in recovering semanticsof the actions and dynamics of missing joints. We will release all the code andmodels publicly.

Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization