Tomohiko Mukai

Geostatistical motion interpolation

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.

Psychological model for animating crowded pedestrians

This paper proposes a psychological model for simulating pedestrian behaviors in a crowded space. Our decision‐making scheme controls plausible avoidance behavior depending on the positional relations among surrounding persons, on the basis of a two‐stage personal space and a virtual memory structure as proposed in social psychology. Our system determines pedestrian walking speed with the crowd density to imitate the measured data in urban engineering, and automatically generates plausible motions of the individual pedestrian by composing a locomotion graph with motion capture data. Our approach based on psychology and a variety of actual measurements can increase the accuracy of simulation at both the micro and macro levels. Copyright

Multilinear Motion Synthesis with Level-of-Detail Controls

Interactive animation systems often use a level-of-detail (LOD) control to reduce the computational cost by eliminating unperceivable details of the scene. Most methods employ a multiresolutional representation of animation and geometrical data, and adaptively change the accuracy level according to the importance of each character. Multilinear analysis provides the efficient representation of multidimensional and multimodal data, including human motion data, based on statistical data correlations. This paper proposes a LOD control method of motion synthesis with a multilinear model. Our method first extracts a small number of principal components of motion samples by analyzing three-mode correlations among joints, time, and samples using high-order singular value decomposition. A new motion is synthesized by interpolating the reduced components using geostatistics, where the prediction accuracy of the resulting motion is controlled by adaptively decreasing the data dimensionality. We introduce a hybrid algorithm to optimize the reduction size and computational time according to the distance from the camera while maintaining visual quality. Our method provides a practical tool for creating an interactive animation of many characters while ensuring accurate and flexible controls at a modest level of computational cost.

Psychological model for animating crowded pedestrians: Virtual Humans and Social Agents

Not everything is perceived as it is provided by the environment. Depending on focus and attention perception can vary and therefore also the knowledge about the world. Virtual humans are sensing the virtual world, storing knowledge and using it to perform tasks. This paper describes our approach to model perceiving, storing and forgetting knowledge as the main regulation of tasks. We use different forms and levels of knowledge which can be independently adapted to different personalities and situations by combining computer graphics methods with psychological models. Copyright

Motion rings for interactive gait synthesis

This paper presents a practical system for synthesizing gait animation in game environments. As well as improving the reality of animation, we should improve the efficiency and the maneuverability of the character, both of which are essential for interactive games. Our system supplies these practical demands by integrating a motion interpolation technique and a sampling-based control mechanism. We introduce a parameterized looped motion data structure, called a motion ring, for synthesizing a variety of cyclic motions. A continuous gait motion is synthesized by circulating through the motion ring while the interpolation parameter is adaptively controlled according to the terrain condition. The gait controller uses a sampling-based precomputation technique which efficiently searches natural foot contact on terrain of an arbitrary surface shape. The interpolation parameter is also controlled to obey the user control within the duration of quarter gait cycle. Although our system slightly sacrifices the physical correctness of the synthesized motion in order to quickly respond to user input, critical visual artifacts such as foot-skating and jerky movement are prevented. We demonstrate the efficiency and versatility of our integrated system by interactively navigating the character on complex, uneven terrain.

Pose-timeline for propagating motion edits

Motion editing often requires repetitive operations for modifying similar action units to give a similar effect or impression. This paper proposes a system for efficiently and flexibly editing the sequence of iterative actions by a few intuitive operations. Our system visualizes a motion sequence on a summary timeline with editable pose-icons, and drag-and-drop operations on the timeline enable intuitive controls of temporal properties of the motion such as timing, duration, and coordination. This graphical interface is also suited to transfer kinematical and temporal features between two motions through simple interactions with a quick preview of the resulting poses. Our method also integrates the concept of edit propagation by which the manual modification of one action unit is automatically transferred to the other units that are robustly detected by similarity search technique. We demonstrate the efficiency of our pose-timeline interface with a propagation mechanism for the timing adjustment of mutual actions and for motion synchronization with a music sequence.

Efficient dynamic skinning with low-rank helper bone controllers

Dynamic skin deformation is vital for creating life-like characters, and its real-time computation is in great demand in interactive applications. We propose a practical method to synthesize plausible and dynamic skin deformation based on a helper bone rig. This method builds helper bone controllers for the deformations caused not only by skeleton poses but also secondary dynamics effects. We introduce a state-space model for a discrete time linear time-invariant system that efficiently maps the skeleton motion to the dynamic movement of the helper bones. Optimal transfer of nonlinear, complicated deformations, including the effect of soft-tissue dynamics, is obtained by learning the training sequence consisting of skeleton motions and corresponding skin deformations. Our approximation method for a dynamics model is highly accurate and efficient owing to its low-rank property obtained by a sparsity-oriented nuclear norm optimization. The resulting linear model is simple enough to easily implement in the existing workflows and graphics pipelines. We demonstrate the superior performance of our method compared to conventional dynamic skinning in terms of computational efficiency including LOD controls, stability in interactive controls, and flexible expression in deformations.

Building helper bone rigs from examples

Helper bone system has been widely used in real-time applications to synthesize high-quality skin deformation with linear blend skinning. Even though this technique provides a flexible yet efficient synthesis for a variety of expressive skin deformations, rigging with helper bones is still a labor-intensive process. In this study, we propose a novel method for building helper bone rigs from examples. We used multiple pairs of skeleton pose and desired skin shapes for our system. First, the system estimates the optimal skinning weights and helper bone transformations to reconstruct each example shape. Next, we construct a regression model which maps a primary skeleton pose to the helper bone transformations. The regression model enables a procedural control over the helper bones according to the primary skeleton. This is done at a lower computational cost and memory footprint. In addition, artists can edit the regression coefficient of the helper bone controller to modify deformation behavior. We demonstrate our systems potential by synthesizing stylized skin deformations in real-time.

Latent nonuniform splines for animation approximation

This paper presents a new method to approximate animation sequences through a nonlinear analysis of the spatiotemporal data. The main idea is to find a spline curve which best approximates a multivariate animation sequence in a reduced subspace. Our method first eliminates data redundancy among multiple animation channels using principal component analysis (PCA). The reduced sequence of latent variables is then approximated by a nonuniform spline with free knots. To solve the highly-nonlinear multimodal problem of the knot optimization, we introduce a stochastic algorithm called covariance matrix adaptation evolution strategy (CMA-ES). Our method optimizes the control points and the free knots using least-square method and CMA-ES, which guarantees the best approximation for arbitrary animation sequences such as mesh animations and motion capture data. Moreover, our method is applicable to practical production pipeline because both PCA-and CMA-based algorithms are computationally stable, efficient, and quasi manual parameter-free. We demonstrate the capability of the proposed method through comparative experiments with a common approximation technique.

Sampling-based Rig Conversion into Non-rigid Helper Bones

While 3D animation packages provide a wide variety of animation rigs for creating expressive skin animation, most interactive systems employ linear blend skinning for hard realtime computation. We propose a method for converting an arbitrary skeleton-driven deformer into a linear blend skinning-based helper bone rig. Our system builds the target rig by applying an example-based skinning technique that uses a minimal training dataset obtained from the source model by two-pass sampling of the skin deformation. The first uniform sampling analyzes the relationship between the rotation of each joint and the deformation of skin vertices. The second sampling composes a minimum training dataset by selecting important pose samples using novel geometrical measures. We also propose a skinning decomposition with similarity transformation algorithm for accurately approximating the non-rigid skin deformation behavior by helper bone transformations. Our experimental results demonstrate the proposed automated rig conversion into non-rigid helper bones from several skeleton-driven deformers, including Delta Mush deformers, corrective blendshapes, and virtual-muscle systems.