Name
Abstract | ||
|---|---|---|
Muscle-based systems have the potential to provide both anatomical accuracy and semantic interpretability as compared to blendshape models; however, a lack of expressivity and differentiability has limited their impact. Thus, we propose modifying a recently developed rather expressive muscle-based system in order to make it fully-differentiable; in fact, our proposed modifications allow this physically robust and anatomically accurate muscle model to conveniently be driven by an underlying blendshape basis. Our formulation is intuitive, natural, as well as monolithically and fully coupled such that one can differentiate the model from end to end, which makes it viable for both optimization and learning-based approaches for a variety of applications. We illustrate this with a number of examples including both shape matching of three-dimensional geometry as as well as the automatic determination of a three-dimensional facial pose from a single two-dimensional RGB image without using markers or depth information. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CVPR.2019.01106 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | Volume |
Interpretability,Pattern recognition,End-to-end principle,Computer science,Rgb image,Artificial intelligence,Expressivity | Journal | abs/1812.02836 |
ISSN | Citations | PageRank |
1063-6919 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Bao | 1 | 14 | 2.35 |
| Matthew Cong | 2 | 30 | 1.60 |
| Stéphane Grabli | 3 | 0 | 0.34 |
| Ronald Fedkiw | 4 | 4772 | 287.40 |