Spectral pose transfer

Yin, Minghao; Li, Guiqing; Lu, Hao; Ouyang, Yaobin; Zhang, Zhibang; Xian, Chuhua

doi:10.1016/j.cagd.2015.03.016

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…Our use of spectral techniques and functional maps for representing deformation fields is also related to previous works in spectral shape processing, including the early approaches of Lévy and colleagues and their extensions [Lévy 2006;Vallet and Lévy 2008;Dey et al 2012] and more recent techniques such those based on coupled quasi-harmonic bases and functional maps [Kovnatsky et al 2013;Yin et al 2015]. In these and related methods deformation fields are represented as triplets of functions, which encode displacement in each spatial coordinate.…”

Section: Related Workmentioning

confidence: 93%

“…Recent improvements of this technique [Kovnatsky et al 2013;Yin et al 2015] include pre-alignment of the spectral basis but the shortcoming are essentially the same. Figure 16 shows that this deformation transfer is by definition extrinsic, orientation dependent and furthermore completely agnostic to the intrinsic structure of the shape.…”

Section: Relation To Existing Techniquesmentioning

confidence: 99%

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Functional Characterization of Deformation Fields

Corman

Ovsjanikov

2019

ACM Trans. Graph.

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In this paper we present a novel representation for deformation fields of 3D shapes, by considering the induced changes in the underlying metric. In particular, our approach allows to represent a deformation field in a coordinate-free way as a linear operator acting on real-valued functions defined on the shape. Such a representation both provides a way to relate deformation fields to other classical functional operators and enables analysis and processing of deformation fields using standard linear-algebraic tools. This opens the door to a wide variety of applications such as explicitly adding extrinsic information into the computation of functional maps, intrinsic shape symmetrization, joint deformation design through precise control of metric distortion, and coordinate-free deformation transfer without requiring pointwise correspondences. Our method is applicable to both surface and volumetric shape representations and we guarantee the equivalence between the operator-based and standard deformation field representation under mild genericity conditions in the discrete setting. We demonstrate the utility of our approach by comparing it with existing techniques and show how our representation provides a powerful toolbox for a wide variety of challenging problems.

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Section: Related Workmentioning

confidence: 93%

Section: Relation To Existing Techniquesmentioning

confidence: 99%

Functional Characterization of Deformation Fields

Corman

Ovsjanikov

2019

ACM Trans. Graph.

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“…Nonetheless, directly exchanging the low‐frequency coefficients results in details loss. Yin et al 13 proposed a detail‐preserving hierarchical spectral pose transfer method, which is low efficiency due to the need for the user to interactively specify and partition the local meshes to be subjected to secondary pose transfer.…”

Section: Related Workmentioning

confidence: 99%

3D mesh pose transfer based on skeletal deformation

Yang

Yin

et al. 2023

Computer Animation & Virtual

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For 3D mesh pose transfer, the target model is obtained by transferring the pose of the reference mesh to the source mesh, where the shape and pose of the source are usually different from that of the reference. In this paper, pose transfer is considered as a deformation process of the source mesh, and we propose a 3D mesh pose transfer method based on skeletal deformation. First, we design a neural network based on the edge convolution operator to extract the skeleton of the 3D mesh and bind the rigid weights; then, we calculate the bone transformations between the two skeletons with different poses and use the diffusion equation to smooth the rigid weights; finally, the source mesh is deformed according to the bone transformations and the smooth weights to get the target mesh. Experiment results on different datasets show that the pose of the reference mesh can be effectively transferred to the source one while maintaining the shape and high‐quality geometric details of the source mesh by using our method.

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“…Recent progress in spectral geometry processing has brought to significant qualitative leaps that lead to better results in a range of challenging tasks such as deformable shape matching [OBS*12; LRBB17], retrieval [RWP06; BBGO11], style [MRC*19] and pose transfer [KBB*13; YLL*15] among others.…”

Section: Introductionmentioning

confidence: 99%

Learning Spectral Unions of Partial Deformable 3D Shapes

Moschella

Melzi

Cosmo

et al. 2022

Computer Graphics Forum

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Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isometry and permutation-invariant representation of a shape. Some recent works show how the intrinsic geometry of a full shape can be recovered from its spectrum, but there are approaches that consider the more challenging problem of recovering the geometry from the spectral information of partial shapes. In this paper, we propose a possible way to fill this gap. We introduce a learning-based method to estimate the Laplacian spectrum of the union of partial non-rigid 3D shapes, without actually computing the 3D geometry of the union or any correspondence between those partial shapes. We do so by operating purely in the spectral domain and by defining the union operation between short sequences of eigenvalues. We show that the approximated union spectrum can be used as-is to reconstruct the complete geometry [MRC*19], perform region localization on a template [RTO*19] and retrieve shapes from a database, generalizing ShapeDNA [RWP06] to work with partialities. Working with eigenvalues allows us to deal with unknown correspondence, different sampling, and different discretizations (point clouds and meshes alike), making this operation especially robust and general. Our approach is data-driven and can generalize to isometric and non-isometric deformations of the surface, as long as these stay within the same semantic class (e.g., human bodies or horses), as well as to partiality artifacts not seen at training time.

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Spectral pose transfer

Cited by 7 publications

References 41 publications

Functional Characterization of Deformation Fields

Functional Characterization of Deformation Fields

3D mesh pose transfer based on skeletal deformation

Learning Spectral Unions of Partial Deformable 3D Shapes

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