This paper presents Master of Puppets (MOP), an animation-by-demonstration framework that allows users to control the motion of virtual characters (puppets) in real time. In the first step, the user is asked to perform the necessary actions that correspond to the character's motions. The user's actions are recorded, and a hidden Markov model (HMM) is used to learn the temporal profile of the actions. During the runtime of the framework, the user controls the motions of the virtual character based on the specified activities. The advantage of the MOP framework is that it recognizes and follows the progress of the user's actions in real time. Based on the forward algorithm, the method predicts the evolution of the user's actions, which corresponds to the evolution of the character's motion. This method treats characters as puppets that can perform only one motion at a time. This means that combinations of motion segments (motion synthesis), as well as the interpolation of individual motion sequences, are not provided as functionalities. By implementing the framework and presenting several computer puppetry scenarios, its efficiency and flexibility in animating virtual characters is demonstrated.
 This paper aims to propose a novel approach to generate new generic human walking patterns using motion-captured data, leading to a real-time engine intended for virtual humans animation. The method applies the PCA (principal component analysis) technique on motion data acquired by an optical system to yield a reduced dimension space where not only interpolation, but also extrapolation are possible, controlled by quantitative speed parameter values. Moreover, with proper normalization and time warping methods, the generic presented engine can produce walking motions with continuously varying human height and speed with real-time reactivity 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 in the parametric space. Our method statistically optimizes interpolation kernels for given parameters at each frame, using a pose distance metric to efficiently analyze the correlation. Motions are accurately predicted for the spatial constraints represented in the parametric space, and they therefore have few undesirable artifacts, if any. This property alleviates the problem of spatial inconsistencies, such as foot-sliding, that are associated with many existing methods. Moreover, numerical estimates for the reliability of predictions enable motions to be adaptively sampled. Since the interpolation kernels are computed with a linear system in real-time, motions can be interactively edited using various spatial controls.  In this paper, we present a motion splicing technique for generating concurrent upper-body actions occurring simultaneously with the evolution of a lower-body locomotion sequence. Specifically, we show that a layered interpolation motion model generates upper-body poses while assigning different actions to each upper-body part. Hence, in the proposed motion splicing approach, it is possible to increase the number of generated motions as well as the number of desired actions that can be performed by virtual characters. Additionally, we propose an iterative motion blending solution, inverse pseudo-blending, to maintain a smooth and natural interaction between the virtual character and the virtual environment; inverse pseudo-blending is a constraint-based motion editing technique that blends the motions enclosed in a tetrahedron by minimising the distances between the end-effector positions of the actual and blended motions. Additionally, to evaluate the proposed solution, we implemented an example-based application for interactive motion splicing based on specified constraints. Finally, the generated results show that the proposed solution can be beneficially applied to interactive applications where concurrent actions of the upper-body are desired. During the past decade, researchers have developed several techniques for transplanting motions. These techniques transplant a partial auxiliary motion, possibly defined for a small set of degrees of freedom, on a base motion. Motion transplantation improves motion databases' expressiveness and allows more control in interactive applications by letting users synthesize body parts separately. However, the auxiliary motion must be properly aligned with the base motion, both temporally and spatially. This article provides an overview of example-based motion transplantation techniques and explains how they determine spatial and temporal alignment between the auxiliary and base motions. It also describes hybrid techniques that can transplant the motions resulting from procedural or physics-based techniques. Facial motion retargeting has been developed mainly in the direction of representing high fidelity between a source and a target model. We present a novel facial motion retargeting method that properly regards the significant characteristics of target face model. We focus on stylistic facial shapes and timings that reveal the individuality of the target model well, after the retargeting process is finished. The method works with a range of expression pairs between the source and the target facial expressions and emotional sequence pairs of the source and the target facial motions. We first construct a prediction model to place semantically corresponding facial shapes. Our hybrid retargeting model, which combines the radial basis function (RBF) and kernel canonical correlation analysis (kCCA)-based regression methods copes well with new input source motions without visual artifacts. 1D Laplacian motion warping follows after the shape retargeting process, replacing stylistically important emotional sequences and thus, representing the characteristics of the target face. Copyright © 2011 John Wiley & Sons, Ltd. Many motion editing algorithms, including transitioning and multitarget interpolation, can be represented as instances of a more general operation called motion blending. We introduce a novel data structure called a registration curve that expands the class of motions that can be successfully blended without manual input. Registration curves achieve this by automatically determining relationships involving the timing, local coordinate frame, and constraints of the input motions. We show how registration curves improve upon existing automatic blending methods and demonstrate their use in common blending operations. Inspired by kernel methods that have been used extensively in achieving efficient facial animation retargeting, this paper presents a solution to retargeting facial animation in virtual character's face model based on the kernel projection of latent structure (KPLS) regression between semantically similar facial expressions. Specifically, a given number of corresponding semantically similar facial expressions are projected into the latent space. By using the Nonlinear Iterative Partial Least Square method, decomposition of the latent variables is achieved. Finally, the KPLS is achieved by solving a kernalized version of the eigenvalue problem. By evaluating our methodology with other kernel-based solutions, the efficiency of the presented methodology in transferring facial animation to face models with different morphological variations is demonstrated. It is increasingly popular to represent non-rigid motion using a deforming mesh sequence: a discrete sequence of frames, each of which is given as a mesh with a common graph structure. Such sequences have the flexibility to represent a wide range of mesh deformations used in practice, but they are also highly redundant, expensive to store, and difficult to edit in a time-coherent manner. We address these limitations with a continuous representation that extracts redundancy in three separate phases, leading to separate editable signals in time, pose and shape. The representation can be applied to any deforming mesh sequence, in contrast to previous domain-specific approaches. By modifying the three signal components, we demonstrate time-coherent editing operations such as local repetition of part of a sequence, frame rate conversion and deformation transfer. We also show that our representation makes it possible to design new deforming sequences simply by sketching a curve in a 2D pose space. © 2012 Wiley Periodicals, Inc.