Dynamic skin deformation is vital for creating life-like characters, and its real-time computation is in great demand in interactive applications. We propose a practical method to synthesize plausible and dynamic skin deformation based on a helper bone rig. This method builds helper bone controllers for the deformations caused not only by skeleton poses but also secondary dynamics effects. We introduce a state-space model for a discrete time linear time-invariant system that efficiently maps the skeleton motion to the dynamic movement of the helper bones. Optimal transfer of non-linear, complicated deformations, including the effect of soft-tissue dynamics, is obtained by learning the training sequence consisting of skeleton motions and corresponding skin deformations. Our approximation method for a dynamics model is highly accurate and efficient owing to its low-rank property obtained by a sparsity-oriented nuclear norm optimization. The resulting linear model is simple enough to easily implement in the existing workflows and graphics pipelines. We demonstrate the superior performance of our method compared to conventional dynamic skinning in terms of computational efficiency including LOD controls, stability in interactive controls, and flexible expression in deformations.
We thank TAISO, Renpoo for making the human character model available at http://www.behind-universe.org/ and for many helpful comments. This work was supported by JSPS KAKENHI Grant Number 15K16110, 15H02704.
Helper bone system has been widely used in real-time applications to synthesize high-quality skin deformation with linear blend skinning. Even though this technique provides a flexible yet efficient synthesis for a variety of expressive skin deformations, rigging with helper bones is still a labor-intensive process. In this study, we propose a novel method for building helper bone rigs from examples. We used multiple pairs of skeleton pose and desired skin shapes for our system. First, the system estimates the optimal skinning weights and helper bone transformations to reconstruct each example shape. Next, we construct a regression model which maps a primary skeleton pose to the helper bone transformations. The regression model enables a procedural control over the helper bones according to the primary skeleton. This is done at a lower computational cost and memory footprint. In addition, artists can edit the regression coefficient of the helper bone controller to modify deformation behavior. We demonstrate our system's potential by synthesizing stylized skin deformations in real-time.
This study was supported in part by Research and Study Project of Tokai University Educational System Research Organization.