Models for character animation

Collins, Gordon; Hilton, Adrian

doi:10.1002/swf.36

Cited by 8 publications

(4 citation statements)

References 75 publications

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“…There are many algorithms used for character animation; Collins and Hilton [2001] provide a survey. We will describe only two animation methods here: skeletal subspace deformation (SSD) and animation space (AS) [Merry et al 2006].…”

Section: Character Animationmentioning

confidence: 99%

Analytic simplification of animated characters

Merry

Marais

Gain

2009

Proceedings of the 6th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa

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a) rest-pose LOD, 337 influences (b) AS LOD, 338 influences (c) inf. simplification, 341 influences Figure 1: Three forms of influence simplification with comparable influence counts. (a) Traditional simplification based on the rest pose collapses the knees and ankles. (b) Simplification in animation space, our first contribution, improves the rear ankles and the tail. (c) Influence simplification, our second contribution, further improves the front legs and the curve of the neck and back. AbstractTraditionally, levels of detail (LOD) for animated characters are computed from a single pose. Later techniques refined this approach by considering a set of sample poses and evaluating a more representative error metric. A recent approach to the character animation problem, animation space, provides a framework for measuring error analytically. The work presented here uses the animation-space framework to derive two new techniques to improve the quality of LOD approximations.Firstly, we use an animation-space distance metric within a progressive mesh-based LOD scheme, giving results that are reasonable across a range of poses, without requiring that the pose space be sampled.Secondly, we simplify individual vertices by reducing the number of bones that influence them, using a constrained least-squares optimisation. This influence simplification is combined with the progressive mesh to form a single stream of simplifications. Influence simplification reduces the geometric error by up to an order of magnitude, and allows models to be simplified further than is possible with only a progressive mesh.Quantitative (geometric error metrics) and qualititative (user perceptual) experiements confirm that these new extensions provide significant improvements in quality over traditional, naïve simplification; and while there is naturally some impact on the speed of the off-line simplification process, it is not prohibitive.

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Section: Character Animationmentioning

confidence: 99%

Analytic simplification of animated characters

Merry

Marais

Gain

2009

Proceedings of the 6th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa

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“…Capturing the shape and motion of the human body, a highly articulated object with complex surfaces, calls first of all for an adequate model. The popular layered models [5] consist of two parts: an articulated stick figure, or skeleton, defining posture, and a skin model, defining the body surface. The skeleton is a kinematic chain of limbs (sticks) linked by joints with rotational degrees of freedom subject to anatomical constraints.…”

Section: Related Workmentioning

confidence: 99%

Human Body Posture via Hierarchical Evolutionary Optimization

Robertson¹,

Trucco²

2006

Procedings of the British Machine Vision Conference 2006

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This paper presents an evolutionary approach to estimating upper-body posture from multi-view markerless sequences. We fit a 24-dof skeleton model to sparse 3-D stereo data from an array of cameras. We use a particle swarm optimization algorithm which is intrinsically parallel, can incorporate constraints and does not require motion models. We subdivide the high-dimensional search space based on limb dynamics from application sequences and perform hierarchical fitting from the least to the most uncertain body parts. We show experimentally the advantages of this scheme against non-hierarchical optimization in terms of sharper error decrease. We report results with 3-D scanner data of a model human and noisy, calibrated stereo disparity maps of a real videoconferencing scene.

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“…For these reasons, we will concentrate only on skeletal animation. The interested reader is referred to Collins and Hilton [2001] for a broader overview of character animation, including a discussion of data acquisition.…”

Section: Background and Related Workmentioning

confidence: 99%

Animation space

2006

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Skeletal subspace deformation (SSD), a simple method of character animation used in many applications, has several shortcomings; the best-known being that joints tend to collapse when bent. We present animation space, a generalization of SSD that greatly reduces these effects and effectively eliminates them for joints that do not have an unusually large range of motion.While other, more expensive generalizations exist, ours is unique in expressing the animation process as a simple linear transformation of the input coordinates. We show that linearity can be used to derive a measure of average distance (across the space of poses), and apply this to improving parametrizations.Linearity also makes it possible to fit a model to a set of examples using least-squares methods. The extra generality in animation space allows for a good fit to realistic data, and overfitting can be controlled to allow fitted models to generalize to new poses. Despite the extra vertex attributes, it is possible to render these animation-space models in hardware with no loss of performance relative to SSD.

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Models for character animation

Cited by 8 publications

References 75 publications

Analytic simplification of animated characters

Analytic simplification of animated characters

Human Body Posture via Hierarchical Evolutionary Optimization

Animation space

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