Name
Abstract | ||
|---|---|---|
ABSTRACT Character rigs are procedural systems that deform a character’s shape driven by a set of rig-control variables. Film quality character rigs are highly complex and therefore computationally expensive and slow to evaluate. We present a machine learning method for approximating facial mesh deformations which reduces rig computations, increases longevity of characters without rig upkeep, and enables portability of proprietary rigs into a variety of external platforms. We perform qualitative and quantitative evaluations on hero characters across several feature films, exhibiting the speed and generality of our approach and demonstrating that our method out performs existing state-of-the-art work on deformation approximations for character faces. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3388767.3407340 | International Conference on Computer Graphics and Interactive Techniques |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah Radzihovsky | 1 | 0 | 0.34 |
| Fernando de Goes | 2 | 450 | 22.74 |
| Mark Meyer | 3 | 19 | 0.94 |