Shape correspondence for cel animation based on a shape association graph and spectral matching

We present an effective spectral matching method based on a shape association graph for finding region correspondences between two cel animation keyframes.We formulate the correspondence problem as an adapted quadratic assignment problem,which comprehensively considers both the intrinsic geometric and topology of regions to find the globally optimal correspondence.To simultaneously represent the geometric and topological similarities between regions,we propose a shape association graph(SAG),whose node attributes indicate the geometric distance between regions,and whose edge attributes indicate the topological distance between combined region pairs.We convert topological distance to geometric distance between geometric objects with topological features of the pairs,and introduce Kendall shape space to calculate the intrinsic geometric distance.By utilizing the spectral properties of the affinity matrix induced by the SAG,our approach can efficiently extract globally optimal region correspondences,even if shapes have inconsistent topology and severe deformation.It is also robust to shapes undergoing similarity transformations,and compatible with parallel computing techniques.

Hierarchical Framework for Shape Correspondence

Detecting similarity between non-rigid shapes is one of the fundamental problems in computer vision.In order to measure the similarity the shapes must first be aligned.As opposite to rigid alignment that can be parameterized using a small number of unknowns representing rotations,reflections and translations,non-rigid alignment is not easily parameterized.Majority of the methods addressing this problem boil down to a minimization of a certain distortion measure.The complexity of a matching process is exponential by nature,but it can be heuristically reduced to a quadratic or even linear for shapes which are smooth two-manifolds.Here we model the shapes using both local and global structures,employ these to construct a quadratic dissimilarity measure,and provide a hierarchical framework for minimizing it to obtain sparse set of corresponding points.These correspondences may serve as an initialization for dense linear correspondence search.

Monocular Video Guided Garment Simulation

We present a prototype to generate a garment-shape sequence guided by a monocular video sequence. It is a combination of a physically-based simulation and a boundary-based modification. Given a garment in the video worn on a mannequin, the simulation generates a garment initial shape by exploiting the mannequin shapes estimated from the video. The modification then deforms the simulated 3D shape into such a shape that matches the garment 2D boundary extracted from the video. According to the matching correspondences between the vertices on the shape and the points on the boundary, the modification is implemented by attracting the matched vertices and their neighboring vertices. For best-matching correspondences and efficient performance, three criteria are introduced to select the candidate vertices for matching. Since modifying each garment shape independently may cause inter-frame oscillations, changes by the modification are also propagated from one frame to the next frame. As a result, the generated garment 3D shape sequence is stable and similar to the garment video sequence. We demonstrate the effectiveness of our prototype with a number of examples.