Name
Abstract | ||
|---|---|---|
We present a fast, fully automatic morphing algorithm for creating simulatable flesh and muscle models for human and humanoid faces. Current techniques for creating such models require a significant amount of time and effort, making them infeasible or impractical. In fact, the vast majority of research papers use only a floating mask with no inner lips, teeth, tongue, eyelids, eyes, head, ears, etc.---and even those that build the full visual model would typically still lack the cranium, jaw, muscles, and other internal anatomy. Our method requires only the target surface mesh as input and can create a variety of models in only a few hours with no user interaction. We start with a symmetric, high resolution, anatomically accurate template model that includes auxiliary information such as feature points and curves. Then given a target mesh, we automatically orient it to the template, detect feature points, and use these to bootstrap the detection of corresponding feature curves. These curve correspondences are used to deform the surface mesh of the template model to match the target mesh. Then, the calculated displacements of the template surface mesh are used to drive a three-dimensional morph of the full template model including all interior anatomy. The resulting target model can be simulated to generate a large range of expressions that are consistent across characters using the same muscle activations. Full automation of this entire process makes it readily available to a wide range of users. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2786784.2786786 | Symposium on Computer Animation |
Field | DocType | Citations |
Morphing,Computer vision,Expression (mathematics),Computer science,Automation,Simulation modeling,Computer facial animation,Artificial intelligence | Conference | 12 |
PageRank | References | Authors |
0.63 | 24 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Cong | 1 | 30 | 1.60 |
| Michael Bao | 2 | 14 | 2.35 |
| L. E. Jane | 3 | 78 | 6.40 |
| Kiran S Bhat | 4 | 141 | 9.40 |
| Ronald Fedkiw | 5 | 4772 | 287.40 |