Data-Driven Analysis and Insight of Human Motion

Abstract

Motion is a central element of the human experience. Artificial Intelligence (AI) and robotics technologies continue to transform society, but work is needed to enable solutions that engage with our motion-driven reality. Critical to an understanding human motion is the ability to model and accurately simulate virtual humans. To that end, my thesis provides data-driven analysis and insight for human motion. I identify two key aspects of realistic human motion simulations: being both \textit{natural} in appearance while covering the rich \textit{variety} of motions exhibited by humans. I describe how motion data can be leveraged to both simulate realistic motion, as well as validate simulation realism through a combination of data-driven analysis and user study approaches. Computational methods for human motion are largely studied in the context of computer graphics and virtual character animation. Drawing from and expanding on work in this field, my work applies data-driven methods for simulating humans in several settings: that of facial motion, local crowd simulation, and global navigation. The methods and analysis in this dissertation present contributions to the fields of AI, robotics, and computer graphics in supporting my thesis that data-driven methods can be used to create and validate realistic simulations of human motion. In the first part of my thesis, I study the simulation of realistic human smiles by conducting a large user study to connect observer reactions to computer animated faces. The result is a rich dataset providing value beyond that of this thesis to interdisciplinary research. I use the data to train a generative model with a new machine learning heuristic (PVL) that I develop, which tunes the trade-offs in creating a variety of happy smiles. I validate the realism of the PVL results with a follow up user study. The second part of my thesis studies the simulation of realistic human navigation. I perform a data-driven evaluation of the impact of collision avoidance on user experiences in virtual reality (VR), validating its importance for enabling the feeling of presence. I leverage motion data of shoppers to drive new insights for human navigation decisions, discovering an entropy law governing item retrieval patterns. Finally, I present a deep-learning technique (SPNets) for simulating realistic human navigation behaviors in indoor settings trained on optimal paths. The resulting agents exhibit several human-like behaviors, such as intelligent backtracking, narrowing down goal locations, and environment familiarity. I validate the realism of SPNet simulations using paths from a user study on the same navigation tasks.