Articulated‐Motion‐Aware Sparse Localized Decomposition

Abstract

Compactly representing time‐varying geometries is an important issue in dynamic geometry processing. This paper proposes a framework of sparse localized decomposition for given animated meshes by analyzing the variation of edge lengths and dihedral angles (LAs) of the meshes. It first computes the length and dihedral angle of each edge for poses and then evaluates the difference (residuals) between the LAs of an arbitrary pose and their counterparts in a reference one. Performing sparse localized decomposition on the residuals yields a set of components which can perfectly capture local motion of articulations. It supports intuitive articulation motion editing through manipulating the blending coefficients of these components. To robustly reconstruct poses from altered LAs, we devise a connection‐map‐based algorithm which consists of two steps of linear optimization. A variety of experiments show that our decomposition is truly localized with respect to rotational motions and outperforms state‐of‐the‐art approaches in precisely capturing local articulated motion.

Multi‐mother wavelet neural network‐based on genetic algorithm and multiresolution analysis for fast 3D mesh deformation

“…Although for the horse dataset, the method [24] has an error equal to 29.6090 and 20.1994 in [21], 18.0624 in [23], 12.9605 in [25], and 7.3682 in [20], compared to 6.4412 in our work. It is clear that the obtained error rate is so reduced and the performance of a MA on the synthesised mesh allows to minimise the resolution without losing the initial data, reaching the control mesh.…”

“…We use the generalisation ability error (root mean square) to ensure the comparison on the set of tests of our method with several state‐of‐the‐art methods, including original sparse localised deformation component (SPLOCS) [20], SPLOCS with deformation gradients [23], SPLOCS with edge lengths and dihedral angles [24], SPLOCS with the feature from [21], and mesh‐based autoencoders for localised deformation component analysis [25].…”

“…Nevertheless, Wang et al . [24] influenced the noise in the dataset and could not recover correctly a number of actions.…”

“…Though, it employs Euclidean coordinates which cannot represent meshes with large rotations. Other works find a solution that solves this limitation by using 3D representations of more advanced forms, with deformation gradients [23] and edge and dihedral angle representations [24]. Unlike the existing approaches, Tan et al .…”

Sparse Data Driven Mesh Deformation

“…However, this work cannot cope with rotations larger than 180 • due to the limitation of deformation gradients. Wang et al [38] extend [36] using rotation invariant features based on [6], and hence have a similar limitation when extrapolation of examples is required. Different from these works [36], [37], [38] which explore sparsity to localize deformation components, our method instead introduces sparse promoting regularization which prefers fewer basis modes for representing deformed shapes.…”