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Chuhua Xian, Junxian Huang, Shuo Jin, Guoliang Luo, Charlie C.L. Wang, "Real-time C^2-Weighting Based Character Skinning Powered by GPU", short paper, Computer Animation and Social Agents 2017, accepted.
Handle driven character skinning is widely favored in animation applications due to its advantages of intuitiveness, effectiveness and simplicity. A research thread to realize this is to compute a proper weighting distribution on points associated with specified handles like points, bars or skeleton, which is critical to the quality of manipulation and can be utilized to produce animation by controlling user handles. In this work, we introduce a new skinning method that is compatible with different model representations, with a key idea of evaluating the local influence of each handle by decomposing the shape domain into small overlapped regions. Thanks to its well-designed formulation, the computation of weights and update of models can be conducted in parallel on GPU, leading to high efficiency and good visual quality supported by the provided experimental and statistical results in this paper.

Shuo Jin, Chengkai Dai, Yang Liu, Charlie C.L. Wang, "Motion Imitation Based on Sparsely Sampled Correspondence", ASME Journal of Computing and Information Science in Engineering, Vol.17, No.4, 041009 (7 pages), June, 2017.
Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human’s poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparsely sampled correspondence, whose generation scheme is also introduced in this paper. Our method is evaluated by applying the human’s motion captured by a RGB-D sensor to a humanoid in real time. Continuous motion can be realized and used in the example application of tele-operation.

Chuhua Xian*, Shuo Jin*, Charlie C.L. Wang, "Efficient C^2-Weighting for Image Warping", IEEE Computer Graphics and Applications, accepted. (*Joint first authors)
Handle-driven image warping based on linear blending is widely used in many applications because of its merits on intuitiveness, efficiency and easiness of implementation. In this paper, we develop a method to compute high-quality weights within a closed domain for image warping. The property of C^2-continuity in weights is guaranteed by the carefully formulated basis functions. The efficiency of our algorithm is ensured by a closed-form formulation of the computation for weights. The cost of inserting a new handle is only the time to evaluate the distances from the new handle to all other sample points in the domain. A virtual handle insertion algorithm is developed to allow users to freely place handles within the domain while preserving the satisfaction of all expected criteria on weights for linear blending. Experimental examples for real-time applications are shown to demonstrate the effectiveness of this method.

Camille Schreck, Damien Rohmer, Stefanie Hahmann, Marie-Paule Cani, Shuo Jin, Charlie C.L. Wang, and Jean-Francis Bloch, "Non-smooth Developable Geometry for Interactively Animating Paper Crumpling", ACM Transactions on Graphics, Vol. 35, No. 1, Article No. 10, December 2015.
We present the first method to animate sheets of paper at interactive rates, while automatically generating a plausible set of sharp features when the sheet is crumpled. The key idea is to interleave standard physically-based simulation steps with procedural generation of a piecewise continuous developable surface. The resulting hybrid surface model captures new singular points dynamically appearing during the crumpling process, mimicking the effect of paper fiber fracture. Although the model evolves over time to take these irreversible damages into account, the mesh used for simulation is kept coarse throughout the animation, leading to efficient computations. Meanwhile, the geometric layer ensures that the surface stays almost isometric to its original 2D pattern. We validate our model through measurements and visual comparison with real paper manipulation, and show results on a variety of crumpled paper configurations.

Kailun Hu, Shuo Jin and Charlie C.L. Wang, "Support Slimming for Single Material Based Additive Manufacturing", Computer-Aided Design, Vol. 65, pp. 1 - 10, August 2015.
In layer-based additive manufacturing (AM), supporting structures need to be inserted to support the overhanging regions. The adding of supporting structures slows down the speed of fabrication and introduces artifacts onto the finished surface. We present an orientation-driven shape optimizer to slim down the supporting structures used in single material-based AM. The optimizer can be employed as a tool to help designers to optimize the original model to achieve a more self-supported shape, which can be used as a reference for their further design. The model to be optimized is first enclosed in a volumetric mesh, which is employed as the domain of computation. The optimizer is driven by the operations of reorientation taken on tetrahedra with "facing-down" surface facets. We formulate the demand on minimizing shape variation as global rigidity energy. The local optimization problem for determining a minimal rotation is analyzed on the Gauss sphere, which leads to a closed-form solution. Moreover, we also extend our approach to create the functions of controlling the deformation and searching for optimal printing directions.

Shuo Jin, Yunbo Zhang, and Charlie C.L. Wang, "Deformation with Enforced Metrics on Length, Area and Volume", Computer Graphics Forum, Special Issue of Eurographics 2014, April 7 - 11, 2014, Strasbourg, France, Vol. 33, No. 2, pp. 429 - 438, April 2014.
Techniques have been developed to deform a mesh with multiple types of constraints. One limitation of prior methods is that the accuracy of demanded metrics on the resultant model cannot be guaranteed. Adding metrics directly as hard constraints to an optimization functional often leads to unexpected distortion when target metrics differ significantly from what are on the input model. In this paper, we present an effective framework to deform mesh models by enforcing demanded metrics on length, area and volume. To approach target metrics stably and minimize distortion, an iterative scale-driven deformation is investigated, and a global optimization functional is exploited to balance the scaling effect at different parts of a model. Examples demonstrate that our approach provides a user-friendly tool for designers who are used to semantic input.