Deformation transfer applies the deformation exhibited by a source triangle mesh onto a different target triangle mesh. Our approach is general and does not require the source and target to share the same number of vertices or triangles, or to have identical connectivity. The user builds a correspondence map between the triangles of the source and those of the target by specifying a small set of vertex markers. Deformation transfer computes the set of transformations induced by the deformation of the source mesh, maps the transformations through the correspondence from the source to the target, and solves an optimization problem to consistently apply the transformations to the target shape. The resulting system of linear equations can be factored once, after which transferring a new deformation to the target mesh requires only a backsubstitution step. Global properties such as foot placement can be achieved by constraining vertex positions. We demonstrate our method by retargeting full body key poses, applying scanned facial deformations onto a digital character, and remapping rigid and non-rigid animation sequences from one mesh onto another.

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Deformation transfer for triangle meshes

The ability to position a small subset of mesh vertices and produce a meaningful overall deformation of the entire mesh is a fundamental task in mesh editing and animation. However, the class of meaningful deformations varies from mesh to mesh and depends on mesh kinematics, which prescribes valid mesh configurations, and a selection mechanism for choosing among them. Drawing an analogy to the traditional use of skeleton-based inverse kinematics for posing skeletons. we define mesh-based inverse kinematics as the problem of finding meaningful mesh deformations that meet specified vertex constraints.Our solution relies on example meshes to indicate the class of meaningful deformations. Each example is represented with a feature vector of deformation gradients that capture the affine transformations which individual triangles undergo relative to a reference pose. To pose a mesh, our algorithm efficiently searches among all meshes with specified vertex positions to find the one that is closest to some pose in a nonlinear span of the example feature vectors. Since the search is not restricted to the span of example shapes, this produces compelling deformations even when the constraints require poses that are different from those observed in the examples. Furthermore, because the span is formed by a nonlinear blend of the example feature vectors, the blending component of our system may also be used independently to pose meshes by specifying blending weights or to compute multi-way morph sequences.

/pdf/mesh-based-inverse-kinematics-ch3dmj54yv.pdf

Mesh-based inverse kinematics

We introduce a new interface for rapidly creating 3D articulated figure animation, from 2D sketches of the character in the desired key frame poses. Since the exact 3D animation corresponding to a set of 2D drawings is ambiguous we first reconstruct the possible 3D configurations and then apply a set of constraints and assumptions to present the user with the most likely 3D pose. The user can refine this candidate pose by choosing among alternate poses proposed by the system. This interface is supported by pose reconstruction and optimization methods specifically designed to work with imprecise hand drawn figures. Our system provides a simple, intuitive and fast interface for creating rough animations that leverages our users' existing ability to draw. The resulting key framed sequence can be exported to commercial animation packages for interpolation and additional refinement.

/pdf/a-sketching-interface-for-articulated-figure-animation-4lv96pqsoq.pdf

A sketching interface for articulated figure animation

This paper presents a method for generating animations of non-humanoid characters from human motion capture data. Characters considered in this work have proportion and/or topology significantly different from humans, but are expected to convey expressions and emotions through body language that are understandable to human viewers. Keyframing is most commonly used to animate such characters. Our method provides an alternative for animating non-humanoid characters that leverages motion data from a human subject performing in the style of the target character. The method consists of a statistical mapping function learned from a small set of corresponding key poses, and a physics-based optimization process to improve the physical realism. We demonstrate our approach on three characters and a variety of motions with emotional expressions.

Animating non-humanoid characters with human motion data

In this paper, we present a technique we call "cartoon capture and retargeting" which we use to track the motion from traditionally animated cartoons and retarget it onto 3-D models, 2-D drawings, ...

/pdf/turning-to-the-masters-261arq5oji.pdf

DeshpandeHrishi

Papers

Turning to the masters