Engineering Commons^™

Semantics-Guided Human Motion Modeling In Virtual Reality Environment, Matthew Korban

LSU Doctoral Dissertations

Human Motion Modeling is essential in Computer Animation and Human-Computer Interaction. This dissertation studies how to enhance the speed and robustness of Human Motion Modeling in Virtual Reality (VR) environments. Specifically, we aim to design a pipeline to effectively capture and use semantic action information to guide the motion capturing from users in physical worlds and its transfer onto digital avatars in VR environments. To recognize the user's action, we first proposed a new Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features from users' skeletal representations. The DDGCN consists of several dynamic feature modeling modules to …

Exploiting Cross Domain Relationships For Target Recognition, Wei Wang

Doctoral Dissertations

Cross domain recognition extracts knowledge from one domain to recognize samples from another domain of interest. The key to solving problems under this umbrella is to find out the latent connections between different domains. In this dissertation, three different cross domain recognition problems are studied by exploiting the relationships between different domains explicitly according to the specific real problems.

First, the problem of cross view action recognition is studied. The same action might seem quite different when observed from different viewpoints. Thus, how to use the training samples from a given camera view and perform recognition in another new view …