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128
SCAPE: shape completion and animation of people
 ACM Trans. Graph
, 2005
"... Figure 1: Animation of a motion capture sequence taken for a subject, of whom we have a single body scan. The muscle deformations are synthesized automatically from the space of pose and body shape deformations. We introduce the SCAPE method (Shape Completion and Animation for PEople) — a datadriv ..."
Abstract

Cited by 172 (4 self)
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Figure 1: Animation of a motion capture sequence taken for a subject, of whom we have a single body scan. The muscle deformations are synthesized automatically from the space of pose and body shape deformations. We introduce the SCAPE method (Shape Completion and Animation for PEople) — a datadriven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and nonrigid deformations. We learn a pose deformation model that derives the nonrigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion — generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a highquality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.
Probabilistic nonlinear principal component analysis with Gaussian process latent variable models
 Journal of Machine Learning Research
, 2005
"... Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure. In this paper we provide an overview of some existing techniques for discovering such embeddings. We then introduce a novel probabilistic interpretation of principal component ..."
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Cited by 134 (14 self)
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Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure. In this paper we provide an overview of some existing techniques for discovering such embeddings. We then introduce a novel probabilistic interpretation of principal component analysis (PCA) that we term dual probabilistic PCA (DPPCA). The DPPCA model has the additional advantage that the linear mappings from the embedded space can easily be nonlinearised through Gaussian processes. We refer to this model as a Gaussian process latent variable model (GPLVM). Through analysis of the GPLVM objective function, we relate the model to popular spectral techniques such as kernel PCA and multidimensional scaling. We then review a practical algorithm for GPLVMs in the context of large data sets and develop it to also handle discrete valued data and missing attributes. We demonstrate the model on a range of realworld and artificially generated data sets.
Automated Extraction and Parameterization of Motions in Large Data Sets
 ACM Transactions on Graphics
, 2004
"... Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively ..."
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Cited by 111 (2 self)
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Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively parameterized space of motions. To find logically similar motions that are numerically dissimilar, our search method employs a novel distance metric to find “close ” motions and then uses them as intermediaries to find more distant motions. Search queries are answered at interactive speeds through a precomputation that compactly represents all possibly similar motion segments. Once a set of related motions has been extracted, we automatically register them and apply blending techniques to create a continuous space of motions. Given a function that defines relevant motion parameters, we present a method for extracting motions from this space that accurately possess new parameters requested by the user. Our algorithm extends previous work by explicitly constraining blend weights to reasonable values and having a runtime cost that is nearly independent of the number of example motions. We present experimental results on a test data set of 37,000 frames, or about ten minutes of motion sampled at 60 Hz.
Learning PhysicsBased Motion Style with Nonlinear Inverse Optimization
 ACM Trans. Graph
, 2005
"... This paper presents a novel physicsbased representation of realistic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, including relative preferences for using some muscles more than others, elastic mechanisms at joints due t ..."
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Cited by 100 (14 self)
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This paper presents a novel physicsbased representation of realistic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, including relative preferences for using some muscles more than others, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints depending on the task. When used in a spacetime optimization framework, the parameters of this model define a wide range of styles of natural human movement.
Peopletrackingbydetection and peopledetectionbytracking
 In CVPR’08
"... Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated occlusions, and cluttered or even moving backgrounds. People detectors have been shown to be able to locate pedestrians even in complex street scene ..."
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Cited by 89 (7 self)
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Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated occlusions, and cluttered or even moving backgrounds. People detectors have been shown to be able to locate pedestrians even in complex street scenes, but false positives have remained frequent. The identification of particular individuals has remained challenging as well. On the other hand, tracking methods are able to find a particular individual in image sequences, but are severely challenged by realworld scenarios such as crowded street scenes. In this paper, we combine the advantages of both detection and tracking in a single framework. The approximate articulation of each person is detected in every frame based on local features that model the appearance of individual body parts. Prior knowledge on possible articulations and temporal coherency within a walking cycle are modeled using a hierarchical Gaussian process latent variable model (hGPLVM). We show how the combination of these results improves hypotheses for position and articulation of each person in several subsequent frames. We present experimental results that demonstrate how this allows to detect and track multiple people in cluttered scenes with reoccurring occlusions. 1.
Gaussian process dynamical models
 In NIPS
, 2006
"... This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a lowdimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closedform, using Gaussian ..."
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Cited by 84 (7 self)
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This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a lowdimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closedform, using Gaussian Process (GP) priors for both the dynamics and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach on human motion capture data in which each pose is 62dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces. Webpage:
Performance Animation from Lowdimensional Control Signals
 ACM Transactions on Graphics
, 2005
"... This paper introduces an approach to performance animation that employs video cameras and a small set of retroreflective markers to create a lowcost, easytouse system that might someday be practical for home use. The lowdimensional control signals from the user's performance are supplemented by ..."
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Cited by 83 (18 self)
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This paper introduces an approach to performance animation that employs video cameras and a small set of retroreflective markers to create a lowcost, easytouse system that might someday be practical for home use. The lowdimensional control signals from the user's performance are supplemented by a database of prerecorded human motion. At run time, the system automatically learns a series of local models from a set of motion capture examples that are a close match to the marker locations captured by the cameras. These local models are then used to reconstruct the motion of the user as a fullbody animation. We demonstrate the power of this approach with realtime control of six different behaviors using two video cameras and a small set of retroreflective markers. We compare the resulting animation to animation from commercial motion capture equipment with a full set of markers.
Meshbased inverse kinematics
 ACM Trans. Graph
, 2005
"... 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 m ..."
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Cited by 72 (7 self)
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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 skeletonbased inverse kinematics for posing skeletons, we define meshbased 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 multiway morph sequences.
Style translation for human motion
 ACM Transactions on Graphics
, 2005
"... Figure 1: Our style translation system transforms a normal walk (TOP) into a sneaky crouch (MIDDLE) and a sideways shuffle (BOTTOM). Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of inte ..."
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Cited by 58 (0 self)
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Figure 1: Our style translation system transforms a normal walk (TOP) into a sneaky crouch (MIDDLE) and a sideways shuffle (BOTTOM). Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of interactive applications which require rapid processing of captured performances. Our solution learns to translate by analyzing differences between performances of the same content in input and output styles. It relies on a novel correspondence algorithm to align motions and a linear timeinvariant model to represent stylistic differences. Once the model is estimated with system identification, the system is capable of translating streaming input with simple linear operations at each frame.
Geostatistical Motion Interpolation
 ACM Transactions on Graphics
, 2005
"... Figure 1: Animations synthesized by our motion interpolation in a 5D parametric space. One parameter changes the style of motion from rough to delicate as shown by the bar indicator. The other four parameters are the heights and widths of two successive steps of stairs for gait motions, and the 2D s ..."
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Cited by 43 (4 self)
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Figure 1: Animations synthesized by our motion interpolation in a 5D parametric space. One parameter changes the style of motion from rough to delicate as shown by the bar indicator. The other four parameters are the heights and widths of two successive steps of stairs for gait motions, and the 2D start and end locations of the box for lifting motions. None of the motions required postcleaning of foot or handsliding. A common motion interpolation technique for realistic human animation is to blend similar motion samples with weighting functions whose parameters are embedded in an abstract space. Existing methods, however, are insensitive to statistical properties, such as correlations between motions. In addition, they lack the capability to quantitatively evaluate the reliability of synthesized motions. This paper proposes a method that treats motion interpolations as statistical predictions of missing data in an arbitrarily definable parametric space. A practical technique of geostatistics, called universal kriging, is then introduced for statistically estimating the correlations between the dissimilarity of motions and the distance