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211
Motion Graphs
, 2002
"... In this paper we present a novel method for creating realistic, controllable motion. Given a corpus of motion capture data, we automatically construct a directed graph called a motion graph that encapsulates connections among the database. The motion graph consists both of pieces of original motion ..."
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Cited by 380 (6 self)
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In this paper we present a novel method for creating realistic, controllable motion. Given a corpus of motion capture data, we automatically construct a directed graph called a motion graph that encapsulates connections among the database. The motion graph consists both of pieces of original motion and automatically generated transitions. Motion can be generated simply by building walks on the graph. We present a general framework for extracting particular graph walks that meet a user's specifications. We then show how this framework can be applied to the specific problem of generating different styles of locomotion along arbitrary paths.
Interactive Control of Avatars Animated with Human Motion Data
, 2002
"... Realtime control of threedimensional avatars is an important problem in the context of computer games and virtual environments. Avatar animation and control is difficult, however, because a large repertoire of avatar behaviors must be made available, and the user must be able to select from this s ..."
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Cited by 369 (38 self)
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Realtime control of threedimensional avatars is an important problem in the context of computer games and virtual environments. Avatar animation and control is difficult, however, because a large repertoire of avatar behaviors must be made available, and the user must be able to select from this set of behaviors, possibly with a lowdimensional input device. One appealing approach to obtaining a rich set of avatar behaviors is to collect an extended, unlabeled sequence of motion data appropriate to the application. In this paper, we show that such a motion database can be preprocessed for flexibility in behavior and efficient search and exploited for realtime avatar control. Flexibility is created by identifying plausible transitions between motion segments, and efficient search through the resulting graph structure is obtained through clustering. Three interface techniques are demonstrated for controlling avatar motion using this data structure: the user selects from a set of available choices, sketches a path through an environment, or acts out a desired motion in front of a video camera. We demonstrate the flexibility of the approach through four different applications and compare the avatar motion to directly recorded human motion.
StyleBased Inverse Kinematics
, 2004
"... This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is repres ..."
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Cited by 211 (8 self)
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This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles. Our stylebased
Motion synthesis from annotations
 ACM Transactions on Graphics
, 2003
"... This paper describes a framework that allows a user to synthesize human motion while retaining control of its qualitative properties. The user paints a timeline with annotations — likewalk, run or jump — from a vocabulary which is freely chosen by the user. The system then assembles frames from a mo ..."
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Cited by 209 (7 self)
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This paper describes a framework that allows a user to synthesize human motion while retaining control of its qualitative properties. The user paints a timeline with annotations — likewalk, run or jump — from a vocabulary which is freely chosen by the user. The system then assembles frames from a motion database so that the final motion performs the specified actions at specified times. The motion can also be forced to pass through particular configurations at particular times, and to go to a particular position and orientation. Annotations can be painted positively (for example, must run), negatively (for example, may not run backwards) orasa don’tcare. The system uses a novel search method, based around dynamic programming at several scales, to obtain a solution efficiently so that authoring is interactive. Our results demonstrate that the method can generate smooth, naturallooking motion. The annotation vocabulary can be chosen to fit the application, and allows specification of composite motions (run andjump simultaneously, for example). The process requires a collection of motion data that has been annotated with the chosen vocabulary. This paper also describes an effective tool, based around repeated use of support vector machines, that allows a user to annotate a large collection of motions quickly and easily so that they may be used with the synthesis algorithm.
Synthesizing physically realistic human motion in lowdimensional, behaviorspecific spaces
 ACM Transactions on Graphics
, 2004
"... Optimization is an appealing way to compute the motion of an animated character because it allows the user to specify the desired motion in a sparse, intuitive way. The difficulty of solving this problem for complex characters such as humans is due in part to the high dimensionality of the search sp ..."
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Cited by 196 (13 self)
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Optimization is an appealing way to compute the motion of an animated character because it allows the user to specify the desired motion in a sparse, intuitive way. The difficulty of solving this problem for complex characters such as humans is due in part to the high dimensionality of the search space. The dimensionality is an artifact of the problem representation because most dynamic human behaviors are intrinsically low dimensional with, for example, legs and arms operating in a coordinated way. We describe a method that exploits this observation to create an optimization problem that is easier to solve. Our method utilizes an existing motion capture database to find a lowdimensional space that captures the properties of the desired behavior. We show that when the optimization problem is solved within this lowdimensional subspace, a sparse sketch can be used as an initial guess and full physics constraints can be enabled. We demonstrate the power of our approach with examples of forward, vertical, and turning jumps; with running and walking; and with several acrobatic flips.
Spacetime faces: High resolution capture for modeling and animation
 IN ACM TRANSACTIONS ON GRAPHICS (PROC. OF ACM SIGGRAPH)
, 2004
"... We present an endtoend system that goes from video sequences to high resolution, editable, dynamically controllable face models. The capture system employs synchronized video cameras and structured light projectors to record videos of a moving face from multiple viewpoints. A novel spacetime stere ..."
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Cited by 193 (7 self)
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We present an endtoend system that goes from video sequences to high resolution, editable, dynamically controllable face models. The capture system employs synchronized video cameras and structured light projectors to record videos of a moving face from multiple viewpoints. A novel spacetime stereo algorithm is introduced to compute depth maps accurately and overcome overfitting deficiencies in prior work. A new template fitting and tracking procedure fills in missing data and yields point correspondence across the entire sequence without using markers. We demonstrate a datadriven, interactive method for inverse kinematics that draws on the large set of fitted templates and allows for posing new expressions by dragging surface points directly. Finally, we describe new tools that model the dynamics in the input sequence to enable new animations, created via keyframing or texturesynthesis techniques.
Videobased face recognition using probabilistic appearance manifolds
 In Proc. IEEE Conference on Computer Vision and Pattern Recognition
, 2003
"... This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a lowdimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), ..."
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Cited by 176 (5 self)
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This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a lowdimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a Kmeans algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing framebased face recognition methods with temporal voting schemes. 1
Gaussian process dynamical models for human motion
 IEEE TRANS. PATTERN ANAL. MACHINE INTELL
, 2008
"... We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from highdimensional motion capture data. A GPDM is a latent variable model. It comprises a lowdimensional latent space with associated dynamics, ..."
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Cited by 158 (5 self)
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We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from highdimensional motion capture data. A GPDM is a latent variable model. It comprises a lowdimensional latent space with associated dynamics, as well as a map from the latent space to an observation space. We marginalize out the model parameters in closed form by using Gaussian process priors for both the dynamical and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach and compare four learning algorithms on human motion capture data, in which each pose is 50dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.
Modeling human motion using binary latent variables
 Advances in Neural Information Processing Systems
, 2006
"... We propose a nonlinear generative model for human motion data that uses an undirected model with binary latent variables and realvalued “visible ” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at th ..."
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Cited by 151 (20 self)
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We propose a nonlinear generative model for human motion data that uses an undirected model with binary latent variables and realvalued “visible ” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few timesteps. Such an architecture makes online inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing online filling in of data lost during motion capture. Website: