Per-actor Based Optimization for Semantic-preserving Facial Rig Generation Using Sample Data
Abstract: With high emphasis on the need of combining recent research and technology regarding automatic facial rig generation with the artistic aspect and the usage of digital humans within film production pipelines, this thesis project presents a scalable blendshape optimization framework that is adapted to fit within a VFX-pipeline, provides stability for various kinds of usage and makes the workflow of creating facial rigs more efficient. The framework successfully generates per-actor based facial rigs adapted towards sample data while ensuring that the semantics of the input rig are kept in the process. With the core in a reusable generic model, gradient based deformations, user-driven regularization terms, rigid alignment, and the possibility to split blendshapes in symmetrical halves, the proposed framework provides a stable algorithm that can be applied to any target blendshape. The proposed framework serves as a source for investigating and evaluating parameters and solutions related to automatic facial rig generation and optimization.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)