Joint-aware manipulation of deformable models

Xu, Weiwei; Wang, Jun; Yin, KangKang; Zhou, Kun; Panne, Michiel van de; Chen, Falai; Guo, Baining

doi:10.1145/1576246.1531341

Cited by 43 publications

(45 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, some methods extract a set of representative primitives from a shape, such as structural curves [Gal et al 2009] or rigid components and joints [Xu et al 2009], so that the shape's defining characteristics are preserved during deformation. Other achievements in high-level analysis include finding the upright orientation of shapes [Fu et al 2008], a property closely related to the functionality of the objects, automatically analyzing an assembly of mechanical parts to infer their motion and chain of interaction [Mitra et al 2010], and building a symmetryinduced hierarchy of man-made shapes [Wang et al 2011].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering

et al. 2011

View full text Add to dashboard Cite

We introduce an algorithm for unsupervised co-segmentation of a set of shapes so as to reveal the semantic shape parts and establish their correspondence across the set. The input set may exhibit significant shape variability where the shapes do not admit proper spatial alignment and the corresponding parts in any pair of shapes may be geometrically dissimilar. Our algorithm can handle such challenging input sets since, first, we perform co-analysis in a descriptor space, where a combination of shape descriptors relates the parts independently of their pose, location, and cardinality. Secondly, we exploit a key enabling feature of the input set, namely, dissimilar parts may be "linked" through third-parties present in the set. The links are derived from the pairwise similarities between the parts' descriptors. To reveal such linkages, which may manifest themselves as anisotropic and non-linear structures in the descriptor space, we perform spectral clustering with the aid of diffusion maps. We show that with our approach, we are able to co-segment sets of shapes that possess significant variability, achieving results that are close to those of a supervised approach.

show abstract

Section: Related Workmentioning

confidence: 99%

Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Recent work on smart deformation tools also includes the method of [CM09], which resizes objects using retargeting of geometry images, and [XWY*09] that try to guess joint properties to build a semi‐articulated deformation model. [WXL*11] and [ZFCO*11] have proposed two symmetry‐based mesh editing techniques that examine symmetry relations in order to derive modification rules.…”

Section: Related Workmentioning

confidence: 99%

Symmetry‐Aware Template Deformation and Fitting

Kurz

Wand

et al. 2014

Computer Graphics Forum

View full text Add to dashboard Cite

In this paper, we propose a new method for reconstructing 3D models from a noisy and incomplete 3D scan and a coarse template model. The main idea is to maintain characteristic high‐level features of the template that remain unchanged for different variants of the same type of object. As invariants, we chose the partial symmetry structure of the template model under Euclidian transformations, i.e. we maintain the algebraic structure of all reflections, rotations and translations that map the object partially to itself. We propose an optimization scheme that maintains continuous and discrete symmetry properties of this kind while registering a template against scan data using a deformable iterative closest points (ICP) framework with thin‐plate‐spline regularization. We apply our new deformation approach to a large number of example data sets and demonstrate that symmetry‐guided template matching often yields much more plausible reconstructions than previous variants of ICP.

show abstract

“…Gal et al [GSMCO09] extend the idea of structure preserving editing by maintaining global geometric relations such as shape symmetries. Slippage analysis has also been used for constraining free‐form deformation towards a more intuitive behavior [XWY*09].…”

Section: Related Workmentioning

confidence: 99%

Mutable elastic models for sculpting structured shapes

Milliez

Wand

Cani

et al. 2013

Computer Graphics Forum

View full text Add to dashboard Cite

In this paper, we propose a new paradigm for free-form shape deformation. Standard deformable models minimize an energy measuring the distance to a single target shape. We propose a new, "mutable" elastic model. It represents complex geometry by a collection of parts and measures the distance of each part measures to a larger set of alternative rest configurations. By detecting and reacting to local switches between best-matching rest states, we build a 3D sculpting system: It takes a structured shape consisting of parts and replacement rules as input. The shape can subsequently be elongated, compressed, bent, cut, and merged within a constraints-based free-form editing interface, where alternative rest-states model to such changes. In practical experiments, we show that the approach yields a surprisingly intuitive and easy to implement interface for interactively designing objects described by such discrete shape grammars, for which direct shape control mechanisms were typically lacking.

show abstract

Joint-aware manipulation of deformable models

Cited by 43 publications

References 42 publications

Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering

Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering

Symmetry‐Aware Template Deformation and Fitting

Mutable elastic models for sculpting structured shapes

Contact Info

Product

Resources

About