The blendshape is an effective tool in computer facial animation, enabling represention of muscle actions. Limitations exist, however, in the level of realism attainable under conventional use of blendshapes as non-intersecting deformations. Using the principle of superposition, it is possible to create a facial model with overlapping blendshapes and achieve more realistic performance. When blendshapes overlap, the region of intersection is in superposition and usually exhibits undesired surface interference. In such cases we use a corrective blendshape to remove the interference automatically. The result is an animatable facial model implemented in Maya which represents the effects of muscle action superposition. Performance created with our model of a known human subject is compared to 3D scan reference data and video reference data of that person. Test animation is compared to video reference footage. The test animation seems to mimic the effects of actual muscle action superposition accurately.

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. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>