@phdthesis{Legde2021, author = {Legde, Katharina}, title = {Projecting motion capture : designing and implementing a modular and flexible facial animation pipeline to evaluate different perceptual effects}, doi = {10.26127/BTUOpen-5604}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-56043}, school = {BTU Cottbus - Senftenberg}, year = {2021}, abstract = {We can not not communicate, humans use body language and facial expressions to communicate verbally and non-verbally with others. Humans are experts at deciphering and understanding facial expressions. Thus, synthesizing them on a virtual face becomes a very challenging task. Realistic facial animations have various applications like movies, games or affective interfaces. Often motion capture recordings of real humans are used to provide the virtual face with realistic motion. Animation controls need to be established to transfer the captured motion onto the virtual face. To mimic the full essence of the captured motion, these animation controls need to be carefully planned beforehand which is accompanied with high effort in terms of spent time and money. The presented thesis would like to offer an alternative approach to the common performance-driven facial animation techniques. Instead of using motion capture to drive a pre-defined blend-shape rig, this thesis projects motion capture directly onto a facial mesh. Throughout the proposed pipeline, different methods for retargeting, rigging and skinning are evaluated. Special attention is given to the fact that the origin of motion capture and the appearance of the virtual face does not need to be identical. A constraint for this approach to work is clean motion capture data. For that, this thesis offers an automatic way to clean facial motion trajectories and establishes stable and coherent motion curves. To not just give insights about the capability of the proposed pipeline but also to provide valuable results in the field of perception of virtual avatars, perceptual experiments are conducted. These experiments reveal the functionality of the pipeline and show that it is possible to re-use motion capture on different virtual faces. Additionally, the proposed pipeline is used as a tool to investigate into the perception of non-verbal communication for virtual avatars.}, subject = {Facial animation; Motion capture; Retargeting; Skinning; Perception; Gesichtsanimation; Motion Capture; Retargeting; Skinning; Wahrnehmung; Gesichtserkennung; Mimik; Merkmalsextraktion; Motion Capturing}, language = {en} }